Content Based RS P3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ratings=pd.read_csv(\"C:/Users/tomar/OneDrive/Desktop/Recommender system/ratings.csv\",encoding=\"ISO-8859-1\")\n",
    "Movies=pd.read_csv(\"C:/Users/tomar/OneDrive/Desktop/Recommender system/movies.csv\",encoding=\"ISO-8859-1\")\n",
    "Tags=pd.read_csv(\"C:/Users/tomar/OneDrive/Desktop/Recommender system/tags.csv\",encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>108</th>\n",
       "      <td>320</td>\n",
       "      <td>260</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1145964264</td>\n",
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      ],
      "text/plain": [
       "     userId  movieId  rating   timestamp\n",
       "108     320      260     4.5  1145964264"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=Ratings[Ratings['userId']==320]\n",
    "a[a['movieId']==260]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "TF= Tags.groupby(['movieId','tag'], as_index = False, sort = False).count().rename(columns = {'userId': 'tag_count_TF'})[['movieId','tag','tag_count_TF']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Tag_distinct = Tags[['tag','movieId']].drop_duplicates()\n",
    "DF =Tag_distinct.groupby(['tag'], as_index = False, sort = False).count().rename(columns = {'movieId': 'tag_count_DF'})[['tag','tag_count_DF']]\n",
    "a=math.log10(len(np.unique(Tags['movieId'])))\n",
    "DF['IDF']=a-np.log10(DF['tag_count_DF'])\n",
    "TF = pd.merge(TF,DF,on = 'tag', how = 'left', sort = False)\n",
    "TF['TF-IDF']=TF['tag_count_TF']*TF['IDF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "Vect_len=TF[['movieId','TF-IDF']]\n",
    "Vect_len['TF-IDF-Sq']=Vect_len['TF-IDF']**2\n",
    "Vect_len =Vect_len.groupby(['movieId'], as_index = False, sort = False).sum().rename(columns = {'TF-IDF-Sq': 'TF-IDF-Sq-sum'})[['movieId','TF-IDF-Sq-sum']]\n",
    "Vect_len['vect_len'] = np.sqrt(Vect_len[['TF-IDF-Sq-sum']].sum(axis=1))\n",
    "TF = pd.merge(TF,Vect_len,on = 'movieId', how = 'left', sort = False)\n",
    "TF['TAG_WT']=TF['TF-IDF']/TF['vect_len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ratings_filter=Ratings[Ratings['rating']>=3.5]\n",
    "distinct_users=np.unique(Ratings['userId'])\n",
    "user_tag_pref=pd.DataFrame()\n",
    "i=1\n",
    "\n",
    "#enter user ID for analysis\n",
    "userID = 320\n",
    "\n",
    "user_index = distinct_users.tolist().index(userID)\n",
    "\n",
    "for user in distinct_users[user_index:user_index+1]:\n",
    "    \n",
    "    if i%30==0:\n",
    "        print (\"user: \", i , \"out of: \", len(distinct_users))\n",
    "            \n",
    "    user_data=  Ratings_filter[Ratings_filter['userId']==user]\n",
    "    user_data = pd.merge(TF,user_data,on = 'movieId', how = 'inner', sort = False)\n",
    "    user_data1 = user_data.groupby(['tag'], as_index = False, sort = False).sum().rename(columns = {'TAG_WT': 'tag_pref'})[['tag','tag_pref']]\n",
    "    user_data1['user']=user\n",
    "    user_tag_pref = user_tag_pref.append(user_data1, ignore_index=True)\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie:  300 out of:  2495 with user:  1 out of:  862\n",
      "movie:  600 out of:  2495 with user:  1 out of:  862\n",
      "movie:  900 out of:  2495 with user:  1 out of:  862\n",
      "movie:  1200 out of:  2495 with user:  1 out of:  862\n",
      "movie:  1500 out of:  2495 with user:  1 out of:  862\n",
      "movie:  1800 out of:  2495 with user:  1 out of:  862\n",
      "movie:  2100 out of:  2495 with user:  1 out of:  862\n",
      "movie:  2400 out of:  2495 with user:  1 out of:  862\n"
     ]
    }
   ],
   "source": [
    "distinct_users=np.unique(Ratings_filter['userId'])\n",
    "tag_merge_all=pd.DataFrame()\n",
    "\n",
    "i=1\n",
    "\n",
    "#enter user ID for analysis\n",
    "userID = 320\n",
    "\n",
    "user_index = distinct_users.tolist().index(userID)\n",
    "\n",
    "for user in distinct_users[user_index:user_index+1]:\n",
    "\n",
    "    \n",
    "    user_tag_pref_all=  user_tag_pref[user_tag_pref['user']==user]\n",
    "    distinct_movies = np.unique(TF['movieId'])\n",
    "    j=1\n",
    "    for movie in distinct_movies:\n",
    "        \n",
    "        if j%300==0:\n",
    "            \n",
    "            print (\"movie: \", j , \"out of: \", len(distinct_movies) , \"with user: \", i , \"out of: \", len(distinct_users))\n",
    "        \n",
    "        TF_Movie=  TF[TF['movieId']==movie]\n",
    "        tag_merge = pd.merge(TF_Movie,user_tag_pref_all,on = 'tag', how = 'left', sort = False)\n",
    "        tag_merge['tag_pref']=tag_merge['tag_pref'].fillna(0)\n",
    "        tag_merge['tag_value']=tag_merge['TAG_WT']*tag_merge['tag_pref']\n",
    "        \n",
    "        TAG_WT_val=np.sqrt(np.sum(np.square(tag_merge['TAG_WT']), axis=0))\n",
    "        tag_pref_val=np.sqrt(np.sum(np.square(user_tag_pref_all['tag_pref']), axis=0))\n",
    "        \n",
    "        tag_merge_final = tag_merge.groupby(['user','movieId'])[['tag_value']].sum().rename(columns = {'tag_value': 'Rating'}).reset_index()\n",
    "        \n",
    "        tag_merge_final['Rating']=tag_merge_final['Rating']/(TAG_WT_val*tag_pref_val)\n",
    "        \n",
    "        tag_merge_all = tag_merge_all.append(tag_merge_final, ignore_index=True)\n",
    "        j=j+1\n",
    "    i=i+1\n",
    "tag_merge_all = tag_merge_all.sort_values(['user','Rating'], ascending=False)\n",
    "\n",
    "# Removing movies already rated by user\n",
    "\n",
    "movies_rated = Ratings[Ratings['userId']==userID]['movieId']\n",
    "tag_merge_all = tag_merge_all[~tag_merge_all['movieId'].isin(movies_rated)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movieId</th>\n",
       "      <th>Rating</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>320.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.349188</td>\n",
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       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>0.306611</td>\n",
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       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>0.297872</td>\n",
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       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>320.0</td>\n",
       "      <td>48394.0</td>\n",
       "      <td>0.288340</td>\n",
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       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>0.287614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>320.0</td>\n",
       "      <td>32587.0</td>\n",
       "      <td>0.287348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>0.283335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>0.278155</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>0.276866</td>\n",
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       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>320.0</td>\n",
       "      <td>7147.0</td>\n",
       "      <td>0.264006</td>\n",
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       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>320.0</td>\n",
       "      <td>7153.0</td>\n",
       "      <td>0.263294</td>\n",
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       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2997.0</td>\n",
       "      <td>0.255072</td>\n",
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       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>320.0</td>\n",
       "      <td>6874.0</td>\n",
       "      <td>0.249789</td>\n",
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       "    <tr>\n",
       "      <th>2320</th>\n",
       "      <td>320.0</td>\n",
       "      <td>68157.0</td>\n",
       "      <td>0.248852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>320.0</td>\n",
       "      <td>48780.0</td>\n",
       "      <td>0.245099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>320.0</td>\n",
       "      <td>60684.0</td>\n",
       "      <td>0.235090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>320.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>0.229968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>320.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.226918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>320.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>0.218728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>320.0</td>\n",
       "      <td>8950.0</td>\n",
       "      <td>0.218560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>320.0</td>\n",
       "      <td>79132.0</td>\n",
       "      <td>0.214290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>320.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>0.214233</td>\n",
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       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>320.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>0.213147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>320.0</td>\n",
       "      <td>57669.0</td>\n",
       "      <td>0.212472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>320.0</td>\n",
       "      <td>48043.0</td>\n",
       "      <td>0.209124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>320.0</td>\n",
       "      <td>97752.0</td>\n",
       "      <td>0.208919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2130</th>\n",
       "      <td>320.0</td>\n",
       "      <td>34405.0</td>\n",
       "      <td>0.207178</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>320.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>0.206741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4873.0</td>\n",
       "      <td>0.206153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>320.0</td>\n",
       "      <td>5378.0</td>\n",
       "      <td>0.203087</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>320.0</td>\n",
       "      <td>8169.0</td>\n",
       "      <td>0.001301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2068</th>\n",
       "      <td>320.0</td>\n",
       "      <td>8907.0</td>\n",
       "      <td>0.001287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>320.0</td>\n",
       "      <td>5135.0</td>\n",
       "      <td>0.001286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>320.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>0.001212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3189.0</td>\n",
       "      <td>0.001140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>0.001120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3675.0</td>\n",
       "      <td>0.001079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>320.0</td>\n",
       "      <td>901.0</td>\n",
       "      <td>0.001072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4368.0</td>\n",
       "      <td>0.001067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>320.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>0.001064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>320.0</td>\n",
       "      <td>6059.0</td>\n",
       "      <td>0.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>320.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.001012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1603.0</td>\n",
       "      <td>0.000959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3999.0</td>\n",
       "      <td>0.000944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2296.0</td>\n",
       "      <td>0.000909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>320.0</td>\n",
       "      <td>5970.0</td>\n",
       "      <td>0.000903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>320.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.000844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>320.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>0.000839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1862.0</td>\n",
       "      <td>0.000818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2724.0</td>\n",
       "      <td>0.000761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>320.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>0.000741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>0.000679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0.000650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>0.000650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2846.0</td>\n",
       "      <td>0.000641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2694.0</td>\n",
       "      <td>0.000605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4499.0</td>\n",
       "      <td>0.000582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>320.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>0.000561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>320.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>0.000317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2353 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user  movieId    Rating\n",
       "29    320.0     32.0  0.349188\n",
       "814   320.0   1748.0  0.306611\n",
       "560   320.0   1206.0  0.297872\n",
       "2200  320.0  48394.0  0.288340\n",
       "554   320.0   1199.0  0.287614\n",
       "2116  320.0  32587.0  0.287348\n",
       "621   320.0   1270.0  0.283335\n",
       "503   320.0   1089.0  0.278155\n",
       "564   320.0   1210.0  0.276866\n",
       "1987  320.0   7147.0  0.264006\n",
       "1990  320.0   7153.0  0.263294\n",
       "1306  320.0   2997.0  0.255072\n",
       "1959  320.0   6874.0  0.249789\n",
       "2320  320.0  68157.0  0.248852\n",
       "2204  320.0  48780.0  0.245099\n",
       "2293  320.0  60684.0  0.235090\n",
       "424   320.0    924.0  0.229968\n",
       "27    320.0     29.0  0.226918\n",
       "316   320.0    589.0  0.218728\n",
       "2074  320.0   8950.0  0.218560\n",
       "2350  320.0  79132.0  0.214290\n",
       "182   320.0    318.0  0.214233\n",
       "333   320.0    628.0  0.213147\n",
       "2275  320.0  57669.0  0.212472\n",
       "2196  320.0  48043.0  0.209124\n",
       "2396  320.0  97752.0  0.208919\n",
       "2130  320.0  34405.0  0.207178\n",
       "180   320.0    316.0  0.206741\n",
       "1748  320.0   4873.0  0.206153\n",
       "1821  320.0   5378.0  0.203087\n",
       "...     ...      ...       ...\n",
       "2033  320.0   8169.0  0.001301\n",
       "2068  320.0   8907.0  0.001287\n",
       "1796  320.0   5135.0  0.001286\n",
       "181   320.0    317.0  0.001212\n",
       "1379  320.0   3189.0  0.001140\n",
       "844   320.0   1885.0  0.001120\n",
       "1515  320.0   3675.0  0.001079\n",
       "403   320.0    901.0  0.001072\n",
       "1689  320.0   4368.0  0.001067\n",
       "203   320.0    355.0  0.001064\n",
       "1889  320.0   6059.0  0.001016\n",
       "159   320.0    276.0  0.001012\n",
       "759   320.0   1603.0  0.000959\n",
       "1612  320.0   3999.0  0.000944\n",
       "1030  320.0   2296.0  0.000909\n",
       "1879  320.0   5970.0  0.000903\n",
       "24    320.0     26.0  0.000844\n",
       "275   320.0    507.0  0.000839\n",
       "837   320.0   1862.0  0.000818\n",
       "1208  320.0   2724.0  0.000761\n",
       "255   320.0    464.0  0.000741\n",
       "550   320.0   1194.0  0.000679\n",
       "459   320.0   1010.0  0.000650\n",
       "852   320.0   1911.0  0.000650\n",
       "1251  320.0   2846.0  0.000641\n",
       "1192  320.0   2694.0  0.000605\n",
       "1706  320.0   4499.0  0.000582\n",
       "224   320.0    381.0  0.000561\n",
       "71    320.0    104.0  0.000457\n",
       "527   320.0   1132.0  0.000317\n",
       "\n",
       "[2353 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_merge_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomar\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "distinct_users=np.unique(Ratings['userId'])\n",
    "user_tag_pref=pd.DataFrame()\n",
    "i=1\n",
    "\n",
    "#enter user ID for analysis\n",
    "userID = 320\n",
    "\n",
    "user_index = distinct_users.tolist().index(userID)\n",
    "\n",
    "for user in distinct_users[user_index:user_index+1]:    \n",
    "    if i%30==0:\n",
    "        print (\"user: \", i , \"out of: \", len(distinct_users))\n",
    "            \n",
    "    user_data=  Ratings[Ratings['userId']==user]\n",
    "    user_data['weight']=user_data[\"rating\"]-user_data[\"rating\"].mean()\n",
    "    user_data1 = pd.merge(TF,user_data,on = 'movieId', how = 'inner', sort = False)\n",
    "    user_data1['TAG_WT_WTD'] = user_data1['TAG_WT']*user_data1['weight']\n",
    "    user_data2 = user_data1.groupby(['tag'], as_index = False, sort = False).sum().rename(columns = {'TAG_WT_WTD': 'tag_pref'})[['tag','tag_pref']]\n",
    "    user_data2['user']=user\n",
    "    user_tag_pref = user_tag_pref.append(user_data2, ignore_index=True)\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie:  300 out of:  2495 with user:  1 out of:  862\n",
      "movie:  600 out of:  2495 with user:  1 out of:  862\n",
      "movie:  900 out of:  2495 with user:  1 out of:  862\n",
      "movie:  1200 out of:  2495 with user:  1 out of:  862\n",
      "movie:  1500 out of:  2495 with user:  1 out of:  862\n",
      "movie:  1800 out of:  2495 with user:  1 out of:  862\n",
      "movie:  2100 out of:  2495 with user:  1 out of:  862\n",
      "movie:  2400 out of:  2495 with user:  1 out of:  862\n"
     ]
    }
   ],
   "source": [
    "distinct_users=np.unique(Ratings_filter['userId'])\n",
    "tag_merge_all=pd.DataFrame()\n",
    "\n",
    "i=1\n",
    "\n",
    "#enter user ID for analysis\n",
    "userID = 320\n",
    "\n",
    "user_index = distinct_users.tolist().index(userID)\n",
    "\n",
    "for user in distinct_users[user_index:user_index+1]:\n",
    "\n",
    "    \n",
    "    user_tag_pref_all=  user_tag_pref[user_tag_pref['user']==user]\n",
    "    distinct_movies = np.unique(TF['movieId'])\n",
    "    j=1\n",
    "    for movie in distinct_movies:\n",
    "        \n",
    "        if j%300==0:\n",
    "            \n",
    "            print (\"movie: \", j , \"out of: \", len(distinct_movies) , \"with user: \", i , \"out of: \", len(distinct_users))\n",
    "        \n",
    "        TF_Movie=  TF[TF['movieId']==movie]\n",
    "        tag_merge = pd.merge(TF_Movie,user_tag_pref_all,on = 'tag', how = 'left', sort = False)\n",
    "        tag_merge['tag_pref']=tag_merge['tag_pref'].fillna(0)\n",
    "        tag_merge['tag_value']=tag_merge['TAG_WT']*tag_merge['tag_pref']\n",
    "        \n",
    "        TAG_WT_val=np.sqrt(np.sum(np.square(tag_merge['TAG_WT']), axis=0))\n",
    "        tag_pref_val=np.sqrt(np.sum(np.square(user_tag_pref_all['tag_pref']), axis=0))\n",
    "        \n",
    "        tag_merge_final = tag_merge.groupby(['user','movieId'])[['tag_value']].sum().rename(columns = {'tag_value': 'Rating'}).reset_index()\n",
    "        \n",
    "        tag_merge_final['Rating']=tag_merge_final['Rating']/(TAG_WT_val*tag_pref_val)\n",
    "        \n",
    "        tag_merge_all = tag_merge_all.append(tag_merge_final, ignore_index=True)\n",
    "        j=j+1\n",
    "    i=i+1\n",
    "tag_merge_all = tag_merge_all.sort_values(['user','Rating'], ascending=False)\n",
    "\n",
    "# Removing movies already rated by user\n",
    "\n",
    "movies_rated = Ratings[Ratings['userId']==userID]['movieId']\n",
    "tag_merge_all = tag_merge_all[~tag_merge_all['movieId'].isin(movies_rated)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movieId</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2216</th>\n",
       "      <td>320.0</td>\n",
       "      <td>48394.0</td>\n",
       "      <td>0.203681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>320.0</td>\n",
       "      <td>7153.0</td>\n",
       "      <td>0.184239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>0.178088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>320.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>0.177350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2997.0</td>\n",
       "      <td>0.175898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>0.165156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>320.0</td>\n",
       "      <td>7147.0</td>\n",
       "      <td>0.161338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>320.0</td>\n",
       "      <td>32587.0</td>\n",
       "      <td>0.160441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>320.0</td>\n",
       "      <td>48780.0</td>\n",
       "      <td>0.159790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>0.154699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1157</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2542.0</td>\n",
       "      <td>0.154045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>320.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>0.153414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>0.153094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>320.0</td>\n",
       "      <td>6874.0</td>\n",
       "      <td>0.152299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>320.0</td>\n",
       "      <td>97752.0</td>\n",
       "      <td>0.152263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>320.0</td>\n",
       "      <td>57669.0</td>\n",
       "      <td>0.151069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>320.0</td>\n",
       "      <td>6807.0</td>\n",
       "      <td>0.150513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>0.147724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>320.0</td>\n",
       "      <td>33493.0</td>\n",
       "      <td>0.142367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>320.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.141658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>320.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>0.140863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4873.0</td>\n",
       "      <td>0.140782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>320.0</td>\n",
       "      <td>68157.0</td>\n",
       "      <td>0.138797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>0.138041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>0.135683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>320.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.134516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>320.0</td>\n",
       "      <td>5378.0</td>\n",
       "      <td>0.129001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4754.0</td>\n",
       "      <td>0.128198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2529.0</td>\n",
       "      <td>0.126029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>0.124947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>-0.054452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>320.0</td>\n",
       "      <td>7154.0</td>\n",
       "      <td>-0.055947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>320.0</td>\n",
       "      <td>57368.0</td>\n",
       "      <td>-0.055998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>320.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>-0.056076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>-0.057353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4487.0</td>\n",
       "      <td>-0.057929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>320.0</td>\n",
       "      <td>748.0</td>\n",
       "      <td>-0.057941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>-0.058350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>320.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>-0.060192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3269.0</td>\n",
       "      <td>-0.060331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2633.0</td>\n",
       "      <td>-0.060659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4639.0</td>\n",
       "      <td>-0.060884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>-0.061288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3841.0</td>\n",
       "      <td>-0.061835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3705.0</td>\n",
       "      <td>-0.063663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1558</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>-0.063924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>320.0</td>\n",
       "      <td>30825.0</td>\n",
       "      <td>-0.064960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4270.0</td>\n",
       "      <td>-0.065277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>320.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>-0.065368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>320.0</td>\n",
       "      <td>4310.0</td>\n",
       "      <td>-0.065452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>320.0</td>\n",
       "      <td>72378.0</td>\n",
       "      <td>-0.065825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>-0.067056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>320.0</td>\n",
       "      <td>61132.0</td>\n",
       "      <td>-0.068148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>320.0</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>-0.069960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>320.0</td>\n",
       "      <td>34048.0</td>\n",
       "      <td>-0.070260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>320.0</td>\n",
       "      <td>91630.0</td>\n",
       "      <td>-0.071626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1412</th>\n",
       "      <td>320.0</td>\n",
       "      <td>3259.0</td>\n",
       "      <td>-0.072350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>320.0</td>\n",
       "      <td>5502.0</td>\n",
       "      <td>-0.072453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>320.0</td>\n",
       "      <td>7163.0</td>\n",
       "      <td>-0.077141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>320.0</td>\n",
       "      <td>2802.0</td>\n",
       "      <td>-0.091720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2369 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user  movieId    Rating\n",
       "2216  320.0  48394.0  0.203681\n",
       "2006  320.0   7153.0  0.184239\n",
       "571   320.0   1210.0  0.178088\n",
       "337   320.0    628.0  0.177350\n",
       "1316  320.0   2997.0  0.175898\n",
       "510   320.0   1089.0  0.165156\n",
       "2003  320.0   7147.0  0.161338\n",
       "2132  320.0  32587.0  0.160441\n",
       "2220  320.0  48780.0  0.159790\n",
       "561   320.0   1199.0  0.154699\n",
       "1157  320.0   2542.0  0.154045\n",
       "183   320.0    318.0  0.153414\n",
       "567   320.0   1206.0  0.153094\n",
       "1975  320.0   6874.0  0.152299\n",
       "2412  320.0  97752.0  0.152263\n",
       "2291  320.0  57669.0  0.151069\n",
       "1969  320.0   6807.0  0.150513\n",
       "833   320.0   1799.0  0.147724\n",
       "2136  320.0  33493.0  0.142367\n",
       "29    320.0     32.0  0.141658\n",
       "374   320.0    778.0  0.140863\n",
       "1763  320.0   4873.0  0.140782\n",
       "2336  320.0  68157.0  0.138797\n",
       "822   320.0   1748.0  0.138041\n",
       "606   320.0   1248.0  0.135683\n",
       "27    320.0     29.0  0.134516\n",
       "1836  320.0   5378.0  0.129001\n",
       "1751  320.0   4754.0  0.128198\n",
       "1150  320.0   2529.0  0.126029\n",
       "777   320.0   1625.0  0.124947\n",
       "...     ...      ...       ...\n",
       "910   320.0   2001.0 -0.054452\n",
       "2007  320.0   7154.0 -0.055947\n",
       "2289  320.0  57368.0 -0.055998\n",
       "366   320.0    743.0 -0.056076\n",
       "530   320.0   1128.0 -0.057353\n",
       "1719  320.0   4487.0 -0.057929\n",
       "368   320.0    748.0 -0.057941\n",
       "1194  320.0   2683.0 -0.058350\n",
       "216   320.0    368.0 -0.060192\n",
       "1422  320.0   3269.0 -0.060331\n",
       "1180  320.0   2633.0 -0.060659\n",
       "1733  320.0   4639.0 -0.060884\n",
       "911   320.0   2002.0 -0.061288\n",
       "1583  320.0   3841.0 -0.061835\n",
       "1543  320.0   3705.0 -0.063663\n",
       "1558  320.0   3753.0 -0.063924\n",
       "2125  320.0  30825.0 -0.064960\n",
       "1684  320.0   4270.0 -0.065277\n",
       "271   320.0    491.0 -0.065368\n",
       "1690  320.0   4310.0 -0.065452\n",
       "2353  320.0  72378.0 -0.065825\n",
       "1238  320.0   2770.0 -0.067056\n",
       "2313  320.0  61132.0 -0.068148\n",
       "521   320.0   1101.0 -0.069960\n",
       "2141  320.0  34048.0 -0.070260\n",
       "2402  320.0  91630.0 -0.071626\n",
       "1412  320.0   3259.0 -0.072350\n",
       "1854  320.0   5502.0 -0.072453\n",
       "2012  320.0   7163.0 -0.077141\n",
       "1249  320.0   2802.0 -0.091720\n",
       "\n",
       "[2369 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_merge_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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