notebooks/recsys_explicit_rating.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Building a Recommender with Ratings Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using GraphLab Create we can take an SFrame containing user ratings for movies, and quickly create a recommender. We'll see how to tune parameters for this recommender and how to get a sense of what its performance will be in practice. Finally (mostly for fun) we'll create two stereotypical users, and see what they get recommended.\n",
      "\n",
      "To start, let's import a few modules."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import graphlab as gl\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Prepare the Data\n",
      "The complete dataset is large. For prototyping on a single machine, let's just use a small sample of all the data. We'll use a sample that contains all ratings for about 12,000 users."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_url = 'https://static.turi.com/datasets/movie_ratings/sample.small'\n",
      "sf = gl.SFrame.read_csv(data_url,delimiter='\\t',column_type_hints={'rating':int})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO] This commercial license of GraphLab Create is assigned to engr@turi.com.\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO] Start server at: ipc:///tmp/graphlab_server-24264 - Server binary: /usr/local/lib/python2.7/dist-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1442615425.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[INFO] GraphLab Server Version: 1.5.2\n"
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       "html": [
        "<pre>PROGRESS: Downloading https://static.turi.com/datasets/movie_ratings/sample.small to /var/tmp/graphlab-toby/24264/000000.small</pre>"
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       "text": [
        "PROGRESS: Downloading https://static.turi.com/datasets/movie_ratings/sample.small to /var/tmp/graphlab-toby/24264/000000.small"
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       "html": [
        "<pre>PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.small</pre>"
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       "text": [
        "PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.small"
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        "<pre>PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.small</pre>"
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       "text": [
        "PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.small"
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      {
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        "<pre>PROGRESS: Parsing completed. Parsed 4000000 lines in 1.22237 secs.</pre>"
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        "PROGRESS: Parsing completed. Parsed 4000000 lines in 1.22237 secs."
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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using the same data to train and evaluate a model is problematic. Specifically this leads to a problem called [overfitting](http://en.wikipedia.org/wiki/Overfitting). We'll follow the common approach of holding out a randomly selected 20% of our data to use later for evaluation."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we just use this helper function to get our train and test set."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(train_set, test_set) = sf.random_split(0.8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have a train and test set, let's come up with a very simple way of predicting ratings. That way when we try more complicated things, we'll have some baseline for comparison.\n",
      "\n",
      "GraphLab's PopularityRecommender provides this functionality. It just stores the mean rating per item. When asked to predict a user's rating for a particular item pair, it just predicts the mean of all ratings for that item; it pays no attention to user information. \n",
      "\n",
      "In order to use the PopularityRecommender, all we need to do is pass its create function the data and tell it the pertinent column names."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = gl.popularity_recommender.create(train_set, 'user', 'movie', 'rating')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>PROGRESS: Recsys training: model = popularity</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Recsys training: model = popularity"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Preparing data set.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Preparing data set."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data prepared in: 1.82253s</pre>"
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       "text": [
        "PROGRESS:     Data prepared in: 1.82253s"
       ]
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      {
       "html": [
        "<pre>PROGRESS: 3199340 observations to process; with 17188 unique items.</pre>"
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       "text": [
        "PROGRESS: 3199340 observations to process; with 17188 unique items."
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have a (simple) model, we need a way to measure the accuracy of its predictions. That way we can compare the performance of different models. The [Root Mean Squared Error](http://en.wikipedia.org/wiki/RMSE) is one of the most common ways to measure the accuracy."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "baseline_rmse = gl.evaluation.rmse(test_set['rating'], m.predict(test_set))\n",
      "print baseline_rmse"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.992343220641\n"
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     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Improving Predictive Accuracy\n",
      "The type of model that turned out to be the best for the famous Netflix Competition is called Matrix Factorization. This is a form of [Collaborative Filtering](http://en.wikipedia.org/wiki/Collaborative_filtering) where recommendations are generated using ratings from users that are some how similar.  \n",
      "\n",
      "Whenever you use a particular type of model there are almost inevitably some parameters you must specify. Matrix Factorization is no exception. Properly tuning these parameters can have a huge effect on how well your model works. The two most important parameters are the number of dimensions and the regularization coefficient.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regularization_vals = [0.001, 0.0001, 0.00001, 0.000001]\n",
      "models = [gl.factorization_recommender.create(train_set, 'user', 'movie', 'rating',\n",
      "                                              max_iterations=50, num_factors=5, regularization=r)\n",
      "          for r in regularization_vals]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>PROGRESS: Recsys training: model = factorization_recommender</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: Recsys training: model = factorization_recommender"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Preparing data set.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Preparing data set."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items.</pre>"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data prepared in: 1.79158s</pre>"
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       "text": [
        "PROGRESS:     Data prepared in: 1.79158s"
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       "html": [
        "<pre>PROGRESS: Training factorization_recommender for recommendations.</pre>"
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       "text": [
        "PROGRESS: Training factorization_recommender for recommendations."
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       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
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        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
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       "html": [
        "<pre>PROGRESS: | Parameter                      | Description                                      | Value    |</pre>"
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        "PROGRESS: | Parameter                      | Description                                      | Value    |"
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        "<pre>PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |</pre>"
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        "PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |"
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        "<pre>PROGRESS: | regularization                 | L2 Regularization on Factors                     | 0.001    |</pre>"
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        "PROGRESS: | regularization                 | L2 Regularization on Factors                     | 0.001    |"
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       "html": [
        "<pre>PROGRESS: | solver                         | Solver used for training                         | sgd      |</pre>"
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        "PROGRESS: | solver                         | Solver used for training                         | sgd      |"
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       "html": [
        "<pre>PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |</pre>"
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        "PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |"
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       "html": [
        "<pre>PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
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       "text": [
        "PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |"
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       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
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       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
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      {
       "html": [
        "<pre>PROGRESS:   Optimizing model using SGD; tuning step size.</pre>"
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        "PROGRESS:   Optimizing model using SGD; tuning step size."
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       "html": [
        "<pre>PROGRESS:   Using 399917 / 3199340 points for tuning the step size.</pre>"
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        "PROGRESS:   Using 399917 / 3199340 points for tuning the step size."
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        "PROGRESS: +---------+-------------------+------------------------------------------+"
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       "html": [
        "<pre>PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
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        "PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |"
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        "PROGRESS: | 1       | 0.00575758        | 0.870597                                 |"
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        "<pre>PROGRESS: | 2       | 0.00287879        | 0.913049                                 |</pre>"
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        "PROGRESS: | 2       | 0.00287879        | 0.913049                                 |"
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        "<pre>PROGRESS: | 3       | 0.0014394         | 0.954261                                 |</pre>"
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        "PROGRESS: | 3       | 0.0014394         | 0.954261                                 |"
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        "<pre>PROGRESS: | 4       | 0.000719698       | 0.990381                                 |</pre>"
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        "PROGRESS: | 4       | 0.000719698       | 0.990381                                 |"
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        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
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        "PROGRESS: +---------+-------------------+------------------------------------------+"
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       "html": [
        "<pre>PROGRESS: | Final   | 0.00575758        | 0.870597                                 |</pre>"
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        "PROGRESS: | Final   | 0.00575758        | 0.870597                                 |"
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        "<pre>PROGRESS: Starting Optimization.</pre>"
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        "PROGRESS: Starting Optimization."
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        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
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       "html": [
        "<pre>PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
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        "PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
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        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
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       "html": [
        "<pre>PROGRESS: | Initial | 82us         | 1.1461            | 1.07056               |             |</pre>"
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        "PROGRESS: | Initial | 82us         | 1.1461            | 1.07056               |             |"
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        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
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       "html": [
        "<pre>PROGRESS: | 1       | 338.838ms    | DIVERGED          | DIVERGED              | 0.00575758  |</pre>"
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        "PROGRESS: | 1       | 338.838ms    | DIVERGED          | DIVERGED              | 0.00575758  |"
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       "html": [
        "<pre>PROGRESS: | RESET   | 417.403ms    | 1.1461            | 1.07056               |             |</pre>"
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       "text": [
        "PROGRESS: | RESET   | 417.403ms    | 1.1461            | 1.07056               |             |"
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      },
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       "html": [
        "<pre>PROGRESS: | 1       | 743.737ms    | DIVERGED          | DIVERGED              | 0.00287879  |</pre>"
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       "text": [
        "PROGRESS: | 1       | 743.737ms    | DIVERGED          | DIVERGED              | 0.00287879  |"
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       "html": [
        "<pre>PROGRESS: | RESET   | 822.623ms    | 1.14612           | 1.07057               |             |</pre>"
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       "text": [
        "PROGRESS: | RESET   | 822.623ms    | 1.14612           | 1.07057               |             |"
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       "html": [
        "<pre>PROGRESS: | 1       | 1.14s        | DIVERGED          | DIVERGED              | 0.0014394   |</pre>"
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       "text": [
        "PROGRESS: | 1       | 1.14s        | DIVERGED          | DIVERGED              | 0.0014394   |"
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       "html": [
        "<pre>PROGRESS: | RESET   | 1.22s        | 1.1461            | 1.07056               |             |</pre>"
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       "text": [
        "PROGRESS: | RESET   | 1.22s        | 1.1461            | 1.07056               |             |"
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       "html": [
        "<pre>PROGRESS: | 1       | 1.48s        | 0.958228          | 0.978889              | 0.000719698 |</pre>"
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       "text": [
        "PROGRESS: | 1       | 1.48s        | 0.958228          | 0.978889              | 0.000719698 |"
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       "html": [
        "<pre>PROGRESS: | 2       | 1.72s        | 0.88606           | 0.941306              | 0.000217921 |</pre>"
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        "PROGRESS: | 2       | 1.72s        | 0.88606           | 0.941306              | 0.000217921 |"
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        "PROGRESS: | 3       | 1.96s        | 0.875021          | 0.935425              | 0.0001284   |"
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        "<pre>PROGRESS: | 4       | 2.28s        | 0.869428          | 0.932431              | 9.10126e-05 |</pre>"
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        "PROGRESS: | 4       | 2.28s        | 0.869428          | 0.932431              | 9.10126e-05 |"
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       "html": [
        "<pre>PROGRESS: | 5       | 2.53s        | 0.865827          | 0.930498              | 7.04879e-05 |</pre>"
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       "text": [
        "PROGRESS: | 5       | 2.53s        | 0.865827          | 0.930498              | 7.04879e-05 |"
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       "html": [
        "<pre>PROGRESS: | 6       | 2.78s        | 0.863238          | 0.929106              | 5.7517e-05  |</pre>"
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        "PROGRESS: | 6       | 2.78s        | 0.863238          | 0.929106              | 5.7517e-05  |"
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       "html": [
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       "text": [
        "PROGRESS: | 7       | 3.10s        | 0.861245          | 0.928033              | 4.85779e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 11      | 4.16s        | 0.856214          | 0.925318              | 2.99555e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 11      | 4.16s        | 0.856214          | 0.925318              | 2.99555e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 12      | 4.43s        | 0.855349          | 0.924851              | 2.73357e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 12      | 4.43s        | 0.855349          | 0.924851              | 2.73357e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 17      | 5.65s        | 0.852209          | 0.923152              | 1.9019e-05  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 17      | 5.65s        | 0.852209          | 0.923152              | 1.9019e-05  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 22      | 6.87s        | 0.850136          | 0.922028              | 1.45824e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 22      | 6.87s        | 0.850136          | 0.922028              | 1.45824e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 27      | 8.09s        | 0.848619          | 0.921205              | 1.18242e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 27      | 8.09s        | 0.848619          | 0.921205              | 1.18242e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 32      | 9.30s        | 0.847437          | 0.920563              | 9.94342e-06 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 32      | 9.30s        | 0.847437          | 0.920563              | 9.94342e-06 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 37      | 10.52s       | 0.846477          | 0.920042              | 8.57885e-06 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 37      | 10.52s       | 0.846477          | 0.920042              | 8.57885e-06 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 42      | 11.75s       | 0.845673          | 0.919605              | 7.54362e-06 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 42      | 11.75s       | 0.845673          | 0.919605              | 7.54362e-06 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 47      | 12.97s       | 0.844986          | 0.919231              | 6.73133e-06 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 47      | 12.97s       | 0.844986          | 0.919231              | 6.73133e-06 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Optimization Complete: Maximum number of passes through the data reached.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Optimization Complete: Maximum number of passes through the data reached."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Computing final objective value and training RMSE.</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: Computing final objective value and training RMSE."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final objective value: 0.844538</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final objective value: 0.844538"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final training RMSE: 0.918988</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final training RMSE: 0.918988"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Recsys training: model = factorization_recommender</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: Recsys training: model = factorization_recommender"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Preparing data set.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Preparing data set."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data prepared in: 1.75709s</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data prepared in: 1.75709s"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Training factorization_recommender for recommendations.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Training factorization_recommender for recommendations."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Parameter                      | Description                                      | Value    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Parameter                      | Description                                      | Value    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | regularization                 | L2 Regularization on Factors                     | 0.0001   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | regularization                 | L2 Regularization on Factors                     | 0.0001   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | solver                         | Solver used for training                         | sgd      |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | solver                         | Solver used for training                         | sgd      |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Optimizing model using SGD; tuning step size.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:   Optimizing model using SGD; tuning step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Using 399917 / 3199340 points for tuning the step size.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:   Using 399917 / 3199340 points for tuning the step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 0       | 0.230303          | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 0       | 0.230303          | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 0.0575758         | No Decrease (2.89944 >= 1.1454)          |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 0.0575758         | No Decrease (2.89944 >= 1.1454)          |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 0.014394          | 0.81024                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 0.014394          | 0.81024                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 0.00719698        | 0.83183                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 0.00719698        | 0.83183                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 0.00359849        | 0.857627                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 0.00359849        | 0.857627                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 0.00179924        | 0.889854                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 0.00179924        | 0.889854                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Final   | 0.014394          | 0.81024                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Final   | 0.014394          | 0.81024                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Starting Optimization.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Starting Optimization."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Initial | 96us         | 1.14612           | 1.07057               |             |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Initial | 96us         | 1.14612           | 1.07057               |             |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 330.484ms    | DIVERGED          | DIVERGED              | 0.014394    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 330.484ms    | DIVERGED          | DIVERGED              | 0.014394    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | RESET   | 406.578ms    | 1.14612           | 1.07057               |             |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | RESET   | 406.578ms    | 1.14612           | 1.07057               |             |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 668.368ms    | 1.10355           | 0.920537              | 0.00719698  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 668.368ms    | 1.10355           | 0.920537              | 0.00719698  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 927.856ms    | 0.912146          | 0.890133              | 0.00217921  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 927.856ms    | 0.912146          | 0.890133              | 0.00217921  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 1.19s        | 0.872743          | 0.884389              | 0.001284    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 1.19s        | 0.872743          | 0.884389              | 0.001284    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 1.46s        | 0.860191          | 0.88408               | 0.000910126 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 1.46s        | 0.860191          | 0.88408               | 0.000910126 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 1.72s        | 0.853122          | 0.884082              | 0.000704879 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 1.72s        | 0.853122          | 0.884082              | 0.000704879 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 6       | 1.98s        | 0.848552          | 0.884224              | 0.00057517  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 6       | 1.98s        | 0.848552          | 0.884224              | 0.00057517  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 9       | 2.73s        | 0.841555          | 0.884854              | 0.000370587 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 9       | 2.73s        | 0.841555          | 0.884854              | 0.000370587 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 11      | 3.23s        | 0.838957          | 0.885186              | 0.000299555 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 11      | 3.23s        | 0.838957          | 0.885186              | 0.000299555 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 14      | 3.98s        | 0.836326          | 0.885536              | 0.000232662 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 14      | 3.98s        | 0.836326          | 0.885536              | 0.000232662 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 19      | 5.24s        | 0.833631          | 0.885951              | 0.000169556 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 19      | 5.24s        | 0.833631          | 0.885951              | 0.000169556 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 24      | 6.52s        | 0.831958          | 0.886253              | 0.000133379 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 24      | 6.52s        | 0.831958          | 0.886253              | 0.000133379 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 29      | 7.80s        | 0.830745          | 0.886457              | 0.000109925 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 29      | 7.80s        | 0.830745          | 0.886457              | 0.000109925 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 34      | 9.09s        | 0.829858          | 0.886629              | 9.34862e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 34      | 9.09s        | 0.829858          | 0.886629              | 9.34862e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 39      | 10.35s       | 0.829149          | 0.886761              | 8.13244e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 39      | 10.35s       | 0.829149          | 0.886761              | 8.13244e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 44      | 11.62s       | 0.828574          | 0.886872              | 7.19626e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 44      | 11.62s       | 0.828574          | 0.886872              | 7.19626e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 49      | 12.88s       | 0.828091          | 0.886962              | 6.45337e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 49      | 12.88s       | 0.828091          | 0.886962              | 6.45337e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Optimization Complete: Maximum number of passes through the data reached.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Optimization Complete: Maximum number of passes through the data reached."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Computing final objective value and training RMSE.</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: Computing final objective value and training RMSE."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final objective value: 0.828566</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final objective value: 0.828566"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final training RMSE: 0.887295</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final training RMSE: 0.887295"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Recsys training: model = factorization_recommender</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Recsys training: model = factorization_recommender"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Preparing data set.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Preparing data set."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items.</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data prepared in: 1.74667s</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data prepared in: 1.74667s"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Training factorization_recommender for recommendations.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Training factorization_recommender for recommendations."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Parameter                      | Description                                      | Value    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Parameter                      | Description                                      | Value    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-05    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-05    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | solver                         | Solver used for training                         | sgd      |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | solver                         | Solver used for training                         | sgd      |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Optimizing model using SGD; tuning step size.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:   Optimizing model using SGD; tuning step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Using 399917 / 3199340 points for tuning the step size.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:   Using 399917 / 3199340 points for tuning the step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 0       | 2.30303           | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 0       | 2.30303           | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 0.575758          | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 0.575758          | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 0.14394           | 0.848182                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 0.14394           | 0.848182                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 0.0719698         | 0.747884                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 0.0719698         | 0.747884                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 0.0359849         | 0.777004                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 0.0359849         | 0.777004                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 0.0179924         | 0.78454                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 0.0179924         | 0.78454                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 6       | 0.00899622        | 0.801716                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 6       | 0.00899622        | 0.801716                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Final   | 0.0719698         | 0.747884                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Final   | 0.0719698         | 0.747884                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Starting Optimization.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Starting Optimization."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Initial | 95us         | 1.14611           | 1.07056               |             |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Initial | 95us         | 1.14611           | 1.07056               |             |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 333.097ms    | DIVERGED          | DIVERGED              | 0.0719698   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 333.097ms    | DIVERGED          | DIVERGED              | 0.0719698   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | RESET   | 409.441ms    | 1.14612           | 1.07057               |             |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | RESET   | 409.441ms    | 1.14612           | 1.07057               |             |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 674.458ms    | 0.945166          | 0.933644              | 0.0359849   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 674.458ms    | 0.945166          | 0.933644              | 0.0359849   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 922.186ms    | 0.833656          | 0.876518              | 0.0167272   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 922.186ms    | 0.833656          | 0.876518              | 0.0167272   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 1.17s        | 0.794613          | 0.851602              | 0.0108961   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 1.17s        | 0.794613          | 0.851602              | 0.0108961   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 1.42s        | 0.78035           | 0.842591              | 0.00807953  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 1.42s        | 0.78035           | 0.842591              | 0.00807953  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 1.67s        | 0.773427          | 0.838109              | 0.00642001  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 1.67s        | 0.773427          | 0.838109              | 0.00642001  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 6       | 1.92s        | 0.769018          | 0.835196              | 0.00532605  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 6       | 1.92s        | 0.769018          | 0.835196              | 0.00532605  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 9       | 2.66s        | 0.76163           | 0.830728              | 0.0035244   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 9       | 2.66s        | 0.76163           | 0.830728              | 0.0035244   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 11      | 3.16s        | 0.758812          | 0.829095              | 0.00287585  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 11      | 3.16s        | 0.758812          | 0.829095              | 0.00287585  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 14      | 3.90s        | 0.756146          | 0.827604              | 0.00225376  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 14      | 3.90s        | 0.756146          | 0.827604              | 0.00225376  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 19      | 5.13s        | 0.753463          | 0.826105              | 0.00165653  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 19      | 5.13s        | 0.753463          | 0.826105              | 0.00165653  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 24      | 6.40s        | 0.751875          | 0.825247              | 0.00130952  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 24      | 6.40s        | 0.751875          | 0.825247              | 0.00130952  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 29      | 7.76s        | 0.750733          | 0.824628              | 0.00108271  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 29      | 7.76s        | 0.750733          | 0.824628              | 0.00108271  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 34      | 9.48s        | 0.749926          | 0.824199              | 0.000922874 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 34      | 9.48s        | 0.749926          | 0.824199              | 0.000922874 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 39      | 11.38s       | 0.749352          | 0.8239                | 0.000804157 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 39      | 11.38s       | 0.749352          | 0.8239                | 0.000804157 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 44      | 13.18s       | 0.748856          | 0.82364               | 0.000712502 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 44      | 13.18s       | 0.748856          | 0.82364               | 0.000712502 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 49      | 14.50s       | 0.748486          | 0.823446              | 0.000639602 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 49      | 14.50s       | 0.748486          | 0.823446              | 0.000639602 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Optimization Complete: Maximum number of passes through the data reached.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Optimization Complete: Maximum number of passes through the data reached."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Computing final objective value and training RMSE.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Computing final objective value and training RMSE."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final objective value: 0.74713</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final objective value: 0.74713"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final training RMSE: 0.822629</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final training RMSE: 0.822629"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Recsys training: model = factorization_recommender</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: Recsys training: model = factorization_recommender"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Preparing data set.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Preparing data set."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data has 3199340 observations with 12020 users and 17188 items."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data prepared in: 1.7611s</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data prepared in: 1.7611s"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Training factorization_recommender for recommendations.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Training factorization_recommender for recommendations."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Parameter                      | Description                                      | Value    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Parameter                      | Description                                      | Value    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-06    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-06    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | solver                         | Solver used for training                         | sgd      |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | solver                         | Solver used for training                         | sgd      |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Optimizing model using SGD; tuning step size.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:   Optimizing model using SGD; tuning step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Using 399917 / 3199340 points for tuning the step size.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:   Using 399917 / 3199340 points for tuning the step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 0       | 23.0303           | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 0       | 23.0303           | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 5.75758           | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 5.75758           | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 1.4394            | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 1.4394            | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 0.359849          | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 0.359849          | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 0.0899622         | 0.64469                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 0.0899622         | 0.64469                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 0.0449811         | 0.776938                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 0.0449811         | 0.776938                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 6       | 0.0224906         | 0.781721                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 6       | 0.0224906         | 0.781721                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 7       | 0.0112453         | 0.795259                                 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 7       | 0.0112453         | 0.795259                                 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Final   | 0.0899622         | 0.64469                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Final   | 0.0899622         | 0.64469                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Starting Optimization.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Starting Optimization."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Initial | 91us         | 1.14611           | 1.07057               |             |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Initial | 91us         | 1.14611           | 1.07057               |             |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 271.257ms    | 1.06564           | 1.02546               | 0.0899622   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 271.257ms    | 1.06564           | 1.02546               | 0.0899622   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 519.436ms    | 0.893836          | 0.9386                | 0.0534919   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 519.436ms    | 0.893836          | 0.9386                | 0.0534919   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 781.26ms     | 0.821313          | 0.898336              | 0.0394657   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 781.26ms     | 0.821313          | 0.898336              | 0.0394657   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 1.02s        | 0.787446          | 0.878677              | 0.0318065   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 1.02s        | 0.787446          | 0.878677              | 0.0318065   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 1.27s        | 0.767935          | 0.867039              | 0.026905    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 1.27s        | 0.767935          | 0.867039              | 0.026905    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 6       | 1.52s        | 0.754864          | 0.859049              | 0.0234664   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 6       | 1.52s        | 0.754864          | 0.859049              | 0.0234664   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 10      | 2.52s        | 0.728348          | 0.842483              | 0.0159978   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 10      | 2.52s        | 0.728348          | 0.842483              | 0.0159978   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 11      | 2.77s        | 0.724747          | 0.840175              | 0.0148942   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 11      | 2.77s        | 0.724747          | 0.840175              | 0.0148942   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 15      | 3.76s        | 0.714201          | 0.833394              | 0.011803    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 15      | 3.76s        | 0.714201          | 0.833394              | 0.011803    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 20      | 4.97s        | 0.706563          | 0.828444              | 0.00951235  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 20      | 4.97s        | 0.706563          | 0.828444              | 0.00951235  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 25      | 6.20s        | 0.701591          | 0.8252                | 0.00804647  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 25      | 6.20s        | 0.701591          | 0.8252                | 0.00804647  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 30      | 7.43s        | 0.698227          | 0.822989              | 0.0070181   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 30      | 7.43s        | 0.698227          | 0.822989              | 0.0070181   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 35      | 8.67s        | 0.695756          | 0.821358              | 0.00625186  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 35      | 8.67s        | 0.695756          | 0.821358              | 0.00625186  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 40      | 9.89s        | 0.693684          | 0.820002              | 0.00565608  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 40      | 9.89s        | 0.693684          | 0.820002              | 0.00565608  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 45      | 11.18s       | 0.692125          | 0.818967              | 0.00517787  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 45      | 11.18s       | 0.692125          | 0.818967              | 0.00517787  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 50      | 12.68s       | 0.690705          | 0.818038              | 0.00478446  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 50      | 12.68s       | 0.690705          | 0.818038              | 0.00478446  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Optimization Complete: Maximum number of passes through the data reached.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Optimization Complete: Maximum number of passes through the data reached."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Computing final objective value and training RMSE.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Computing final objective value and training RMSE."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final objective value: 0.678585</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final objective value: 0.678585"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final training RMSE: 0.810596</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final training RMSE: 0.810596"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Save the train and test RMSE, for each model\n",
      "(rmse_train, rmse_test) = ([], [])\n",
      "for m in models:\n",
      "    rmse_train.append(m['training_rmse'])\n",
      "    rmse_test.append(gl.evaluation.rmse(test_set['rating'], m.predict(test_set)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's create a plot to show the RMSE for these different regularization values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(fig, ax) = plt.subplots(figsize=(10, 8))\n",
      "[p1, p2, p3] = ax.semilogx(regularization_vals, rmse_train, \n",
      "                           regularization_vals, rmse_test, \n",
      "                           regularization_vals, len(regularization_vals) * [baseline_rmse]\n",
      "                           )\n",
      "ax.set_ylim([0.7, 1.1])\n",
      "ax.set_xlabel('Regularization', fontsize=20)\n",
      "ax.set_ylabel('RMSE', fontsize=20)\n",
      "ax.legend([p1, p2, p3], [\"Train\", \"Test\", \"Baseline\"])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x7f3b984ea650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2d+6uIe6FF2DAgN2fsXrYYU50UPl19D137yILdJ8EBpE9Z/YnZDNoO12o28Fw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\nv+fqDsr6Pc9ruGvJM2ulrsrvuboDv+f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       "text": [
        "<matplotlib.figure.Figure at 0x7f3b9ae17150>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like we get the best Root Mean Squared Error (on the test set) when n_factors = 5 and regularization = 0.00001. Let's use those parameters!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Load a Larger Dataset\n",
      "Now let's train a new model (using the parameters we've picked) with a larger sample. This time we'll use a dataset that contains all ratings for about 30,000 users."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_url = 'https://static.turi.com/datasets/movie_ratings/sample.large'\n",
      "data = gl.SFrame.read_csv(data_url,delimiter='\\t',column_type_hints={'rating':int})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>PROGRESS: Downloading https://static.turi.com/datasets/movie_ratings/sample.large to /var/tmp/graphlab-toby/24264/000001.large</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Downloading https://static.turi.com/datasets/movie_ratings/sample.large to /var/tmp/graphlab-toby/24264/000001.large"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.large</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.large"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Parsing completed. Parsed 100 lines in 0.74785 secs.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Parsing completed. Parsed 100 lines in 0.74785 secs."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Read 1549015 lines. Lines per second: 2.21292e+06</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Read 1549015 lines. Lines per second: 2.21292e+06"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.large</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Finished parsing file https://static.turi.com/datasets/movie_ratings/sample.large"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Parsing completed. Parsed 10000000 lines in 2.74104 secs.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Parsing completed. Parsed 10000000 lines in 2.74104 secs."
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# The Action Lover and the Romance Lover\n",
      "Let's see how this model behaves for two simulated/stereotypical users who have only rated a small number movies. We'll create two test users. One who loves action movies and hates romance movies. The other loves romance movies and hates actions movies. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "action_movies = ['GoldenEye', 'Casino Royale', 'Independence Day', 'Con Air', 'The Rock', \n",
      "               'The Bourne Identity', 'Ocean\\'s Eleven', 'Lethal Weapon 4', 'Gladiator', \n",
      "               'Indiana Jones and the Last Crusade', 'The Matrix', 'Kill Bill: Vol. 1',\n",
      "               'Air Force One', 'Braveheart', 'The Man with the Golden Gun',\n",
      "               'The Bourne Supremacy', 'Saving Private Ryan']\n",
      "\n",
      "romance_movies = ['Sleepless in Seattle', 'An Affair to Remember', 'Ghost', 'Love Actually',\n",
      "                  'You\\'ve Got Mail', 'Notting Hill', 'Titanic', 'Miss Congeniality',\n",
      "                  'Some Like It Hot', 'Pretty Woman', 'How to Lose a Guy in 10 Days']\n",
      "\n",
      "# Boring helper function to create ratings\n",
      "def ratings(movie_list, user, rating):\n",
      "    num = len(movie_list)\n",
      "    records = {'user': [user] * num, 'movie': movie_list, 'rating': [rating] * num}\n",
      "    return gl.SFrame(records)\n",
      "\n",
      "# Loves action movies, hates romance movies\n",
      "action_user = 'Archie the Action Lover'\n",
      "action_user_ratings = ratings(action_movies, action_user, 5)\n",
      "action_user_ratings = action_user_ratings.append(ratings(romance_movies, action_user, 1))\n",
      "\n",
      "# Loves romance movies, hates action movies\n",
      "romantic_user = 'Rebecca the Romance Lover'\n",
      "romantic_user_ratings = ratings(action_movies, romantic_user, 1)\n",
      "romantic_user_ratings = romantic_user_ratings.append(ratings(romance_movies, romantic_user, 5))\n",
      "\n",
      "data = data.append(action_user_ratings)\n",
      "data = data.append(romantic_user_ratings)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's create a matrix factorization model, using the larger data sample."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a new model, using the larger dataset, with the tuned parameters\n",
      "m = gl.ranking_factorization_recommender.create(data, 'user', 'movie', 'rating', \n",
      "                                                max_iterations=50, num_factors=5,\n",
      "                                                regularization=0.00001)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>PROGRESS: Recsys training: model = ranking_factorization_recommender</pre>"
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       "text": [
        "PROGRESS: Recsys training: model = ranking_factorization_recommender"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Preparing data set.</pre>"
       ],
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       "text": [
        "PROGRESS: Preparing data set."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data has 10000056 observations with 30436 users and 17284 items.</pre>"
       ],
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       "text": [
        "PROGRESS:     Data has 10000056 observations with 30436 users and 17284 items."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:     Data prepared in: 5.91371s</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS:     Data prepared in: 5.91371s"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Training ranking_factorization_recommender for recommendations.</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: Training ranking_factorization_recommender for recommendations."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Parameter                      | Description                                      | Value    |</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: | Parameter                      | Description                                      | Value    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | num_factors                    | Factor Dimension                                 | 5        |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-05    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-05    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | solver                         | Solver used for training                         | sgd      |</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS: | solver                         | Solver used for training                         | sgd      |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | ranking_regularization         | Rank-based Regularization Weight                 | 0.25     |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | ranking_regularization         | Rank-based Regularization Weight                 | 0.25     |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 50       |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +--------------------------------+--------------------------------------------------+----------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +--------------------------------+--------------------------------------------------+----------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Optimizing model using SGD; tuning step size.</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS:   Optimizing model using SGD; tuning step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:   Using 1250007 / 10000056 points for tuning the step size.</pre>"
       ],
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       "output_type": "display_data",
       "text": [
        "PROGRESS:   Using 1250007 / 10000056 points for tuning the step size."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 0       | 0.73682           | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 0       | 0.73682           | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 0.184205          | Not Viable                               |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 0.184205          | Not Viable                               |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 0.0460513         | 1.43195                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 0.0460513         | 1.43195                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 0.0230256         | 1.49754                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 0.0230256         | 1.49754                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 0.0115128         | 1.51573                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 0.0115128         | 1.51573                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 0.00575641        | 1.55046                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 0.00575641        | 1.55046                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Final   | 0.0460513         | 1.43195                                  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Final   | 0.0460513         | 1.43195                                  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+-------------------+------------------------------------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+-------------------+------------------------------------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Starting Optimization.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Starting Optimization."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | Initial | 276us        | 2.21269           | 1.06232               |             |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | Initial | 276us        | 2.21269           | 1.06232               |             |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 3.03s        | DIVERGED          | DIVERGED              | 0.0460513   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 3.03s        | DIVERGED          | DIVERGED              | 0.0460513   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | RESET   | 3.87s        | 2.21268           | 1.06232               |             |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | RESET   | 3.87s        | 2.21268           | 1.06232               |             |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 1       | 6.10s        | 1.76515           | 1.04168               | 0.0230256   |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 1       | 6.10s        | 1.76515           | 1.04168               | 0.0230256   |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 2       | 8.29s        | 1.496             | 0.988318              | 0.00697202  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 2       | 8.29s        | 1.496             | 0.988318              | 0.00697202  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 3       | 10.55s       | 1.39915           | 0.943215              | 0.00410794  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 3       | 10.55s       | 1.39915           | 0.943215              | 0.00410794  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 4       | 12.88s       | 1.37948           | 0.937378              | 0.00291179  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 4       | 12.88s       | 1.37948           | 0.937378              | 0.00291179  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 5       | 15.09s       | 1.3731            | 0.934749              | 0.00225514  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 5       | 15.09s       | 1.3731            | 0.934749              | 0.00225514  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 6       | 17.39s       | 1.36922           | 0.933352              | 0.00184015  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 6       | 17.39s       | 1.36922           | 0.933352              | 0.00184015  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 7       | 19.99s       | 1.36645           | 0.932667              | 0.00155416  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 7       | 19.99s       | 1.36645           | 0.932667              | 0.00155416  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 8       | 22.60s       | 1.36499           | 0.932007              | 0.00134511  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 8       | 22.60s       | 1.36499           | 0.932007              | 0.00134511  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 9       | 24.99s       | 1.36376           | 0.93172               | 0.00118563  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 9       | 24.99s       | 1.36376           | 0.93172               | 0.00118563  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 10      | 27.20s       | 1.36259           | 0.931271              | 0.00105996  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 10      | 27.20s       | 1.36259           | 0.931271              | 0.00105996  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 11      | 29.41s       | 1.36159           | 0.931036              | 0.000958373 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 11      | 29.41s       | 1.36159           | 0.931036              | 0.000958373 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 12      | 31.63s       | 1.36065           | 0.930814              | 0.000874557 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 12      | 31.63s       | 1.36065           | 0.930814              | 0.000874557 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 13      | 33.93s       | 1.36026           | 0.930647              | 0.000804223 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 13      | 33.93s       | 1.36026           | 0.930647              | 0.000804223 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 14      | 36.12s       | 1.35963           | 0.930484              | 0.000744359 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 14      | 36.12s       | 1.35963           | 0.930484              | 0.000744359 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 15      | 38.33s       | 1.35922           | 0.93036               | 0.000692791 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 15      | 38.33s       | 1.35922           | 0.93036               | 0.000692791 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 16      | 40.55s       | 1.35868           | 0.930279              | 0.000647904 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 16      | 40.55s       | 1.35868           | 0.930279              | 0.000647904 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 17      | 42.76s       | 1.35827           | 0.930106              | 0.00060848  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 17      | 42.76s       | 1.35827           | 0.930106              | 0.00060848  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 18      | 44.96s       | 1.35792           | 0.930096              | 0.000573579 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 18      | 44.96s       | 1.35792           | 0.930096              | 0.000573579 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 19      | 47.17s       | 1.35757           | 0.929963              | 0.000542464 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 19      | 47.17s       | 1.35757           | 0.929963              | 0.000542464 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 20      | 49.37s       | 1.35714           | 0.929868              | 0.000514551 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 20      | 49.37s       | 1.35714           | 0.929868              | 0.000514551 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 21      | 51.57s       | 1.357             | 0.929816              | 0.000489371 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 21      | 51.57s       | 1.357             | 0.929816              | 0.000489371 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 22      | 53.79s       | 1.35692           | 0.929796              | 0.000466539 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 22      | 53.79s       | 1.35692           | 0.929796              | 0.000466539 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 23      | 56.00s       | 1.35664           | 0.929771              | 0.000445744 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 23      | 56.00s       | 1.35664           | 0.929771              | 0.000445744 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 24      | 58.17s       | 1.3567            | 0.929697              | 0.000426723 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 24      | 58.17s       | 1.3567            | 0.929697              | 0.000426723 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 25      | 1m 0s        | 1.35615           | 0.929617              | 0.000407887 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 25      | 1m 0s        | 1.35615           | 0.929617              | 0.000407887 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 26      | 1m 2s        | 1.35614           | 0.929608              | 0.000390406 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 26      | 1m 2s        | 1.35614           | 0.929608              | 0.000390406 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 27      | 1m 4s        | 1.35604           | 0.92957               | 0.000374104 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 27      | 1m 4s        | 1.35604           | 0.92957               | 0.000374104 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 28      | 1m 7s        | 1.35573           | 0.929503              | 0.000360613 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 28      | 1m 7s        | 1.35573           | 0.929503              | 0.000360613 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 29      | 1m 9s        | 1.35573           | 0.929508              | 0.000348061 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 29      | 1m 9s        | 1.35573           | 0.929508              | 0.000348061 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 30      | 1m 11s       | 1.35551           | 0.929469              | 0.000336354 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 30      | 1m 11s       | 1.35551           | 0.929469              | 0.000336354 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 31      | 1m 13s       | 1.3554            | 0.929429              | 0.000325409 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 31      | 1m 13s       | 1.3554            | 0.929429              | 0.000325409 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 32      | 1m 15s       | 1.3553            | 0.929405              | 0.000315153 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 32      | 1m 15s       | 1.3553            | 0.929405              | 0.000315153 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 33      | 1m 18s       | 1.35519           | 0.929338              | 0.000305525 |</pre>"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 33      | 1m 18s       | 1.35519           | 0.929338              | 0.000305525 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 34      | 1m 20s       | 1.35522           | 0.92938               | 0.000296467 |</pre>"
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       "output_type": "display_data",
       "text": [
        "PROGRESS: | 34      | 1m 20s       | 1.35522           | 0.92938               | 0.000296467 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 35      | 1m 22s       | 1.35495           | 0.929319              | 0.000286789 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 35      | 1m 22s       | 1.35495           | 0.929319              | 0.000286789 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 36      | 1m 24s       | 1.35502           | 0.929336              | 0.000277522 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 36      | 1m 24s       | 1.35502           | 0.929336              | 0.000277522 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 37      | 1m 26s       | 1.3547            | 0.929303              | 0.000268611 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 37      | 1m 26s       | 1.3547            | 0.929303              | 0.000268611 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 38      | 1m 29s       | 1.35472           | 0.929248              | 0.000260004 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 38      | 1m 29s       | 1.35472           | 0.929248              | 0.000260004 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 39      | 1m 31s       | 1.35461           | 0.929242              | 0.000251652 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 39      | 1m 31s       | 1.35461           | 0.929242              | 0.000251652 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 40      | 1m 33s       | 1.35469           | 0.929233              | 0.00024351  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 40      | 1m 33s       | 1.35469           | 0.929233              | 0.00024351  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 41      | 1m 35s       | 1.35429           | 0.929219              | 0.000235532 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 41      | 1m 35s       | 1.35429           | 0.929219              | 0.000235532 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 42      | 1m 38s       | 1.35422           | 0.929119              | 0.000207464 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 42      | 1m 38s       | 1.35422           | 0.929119              | 0.000207464 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 43      | 1m 40s       | 1.35385           | 0.928963              | 0.000171404 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 43      | 1m 40s       | 1.35385           | 0.928963              | 0.000171404 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 44      | 1m 42s       | 1.35384           | 0.928982              | 0.000168474 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 44      | 1m 42s       | 1.35384           | 0.928982              | 0.000168474 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 45      | 1m 44s       | 1.35377           | 0.928969              | 0.000165658 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 45      | 1m 44s       | 1.35377           | 0.928969              | 0.000165658 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 46      | 1m 46s       | 1.35392           | 0.928978              | 0.00016295  |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 46      | 1m 46s       | 1.35392           | 0.928978              | 0.00016295  |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 47      | 1m 49s       | 1.35343           | 0.928864              | 0.000134831 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 47      | 1m 49s       | 1.35343           | 0.928864              | 0.000134831 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 48      | 1m 51s       | 1.35317           | 0.928789              | 0.000111603 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 48      | 1m 51s       | 1.35317           | 0.928789              | 0.000111603 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 49      | 1m 53s       | 1.35328           | 0.928718              | 9.24064e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 49      | 1m 53s       | 1.35328           | 0.928718              | 9.24064e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: | 50      | 1m 55s       | 1.35305           | 0.928652              | 7.65357e-05 |</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: | 50      | 1m 55s       | 1.35305           | 0.928652              | 7.65357e-05 |"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Optimization Complete: Maximum number of passes through the data reached.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Optimization Complete: Maximum number of passes through the data reached."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS: Computing final objective value and training RMSE.</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS: Computing final objective value and training RMSE."
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final objective value: 1.36312</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final objective value: 1.36312"
       ]
      },
      {
       "html": [
        "<pre>PROGRESS:        Final training RMSE: 0.929411</pre>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "PROGRESS:        Final training RMSE: 0.929411"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let see what recommendations we get for our action lover."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Show recommendations for the action lover.\n",
      "recommendations = m.recommend(gl.SArray([action_user]), k=40)\n",
      "print recommendations['movie']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Lord of the Rings: The Return of the King: Extended Edition', 'The Lord of the Rings: The Fellowship of the Ring: Extended Edition', 'Lord of the Rings: The Return of the King', 'Lord of the Rings: The Two Towers: Extended Edition', 'Star Wars: Episode V: The Empire Strikes Back', 'Lord of the Rings: The Two Towers', 'Raiders of the Lost Ark', 'Star Wars: Episode IV: A New Hope', 'The Shawshank Redemption: Special Edition', 'Lord of the Rings: The Fellowship of the Ring', 'Star Wars: Episode VI: Return of the Jedi', 'GoodFellas: Special Edition', 'The Silence of the Lambs', 'Band of Brothers', 'Pulp Fiction', 'The Godfather', 'The Usual Suspects', 'Full Metal Jacket', 'Seven', 'The Terminator', 'The Sopranos: Season 1', 'Scarface: 20th Anniversary Edition', 'The Sopranos: Season 2', 'The Incredibles', \"National Lampoon's Animal House\", 'Die Hard', 'Batman Begins', 'Terminator 2: Extreme Edition', 'Forrest Gump', 'The Sixth Sense', 'Caddyshack', \"Schindler's List\", 'The Sopranos: Season 4', 'The Green Mile', 'The Sopranos: Season 3', \"Aliens: Collector's Edition\", 'Reservoir Dogs', 'Indiana Jones and the Temple of Doom', 'The Shining', \"Alien: Collector's Edition\"]\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let see what recommendations we get for our romance lover."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Show recommendations for the romance lover.\n",
      "recommendations = m.recommend(gl.SArray([romantic_user]), k=40)\n",
      "print recommendations['movie']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Sex and the City: Season 6: Part 1', 'Steel Magnolias', 'Sex and the City: Season 6: Part 2', 'Sex and the City: Season 4', 'Sex and the City: Season 5', 'Sex and the City: Season 3', 'Sex and the City: Season 2', 'The Sound of Music', 'Fried Green Tomatoes', 'Sex and the City: Season 1', 'The Notebook', 'Finding Nemo (Widescreen)', \"Gone with the Wind: Collector's Edition\", 'Sense and Sensibility', 'Beaches', 'Hotel Rwanda', 'The Shawshank Redemption: Special Edition', 'Pride and Prejudice', 'My Big Fat Greek Wedding', 'When Harry Met Sally', 'Dirty Dancing', \"Schindler's List\", 'Mary Poppins', 'My Fair Lady: Special Edition', 'Chocolat', 'Bend It Like Beckham', \"Bridget Jones's Diary\", 'Erin Brockovich', 'To Kill a Mockingbird', \"The Wizard of Oz: Collector's Edition\", \"Something's Gotta Give\", 'Philadelphia', 'Shrek (Full-screen)', 'Finding Neverland', 'The Sixth Sense', 'Terms of Endearment', 'Calendar Girls', 'Life Is Beautiful', 'Lord of the Rings: The Return of the King: Extended Edition', 'The Lord of the Rings: The Fellowship of the Ring: Extended Edition']\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks good to me, especially considering these users haven't rated many movies!\n",
      "\n",
      "(Looking for more details about the modules and functions? Check out the <a href=\"https://turi.com/products/create/docs/\">API docs</a>.)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}