updated project 4 desc

Projects_StarterCodes/Project4-RecommenderSystem/doc/project4_desc.md

@@ -1,21 +1,28 @@
 ### GR5243 Fall 2019 Applied Data Science
 ### Project 4 Collaborative Filtering Algorithms Evaluation
 
-In this project, working in teams, you will evaluate and **compare** algorithms for **Collaborative Filtering**.
+In this project, working in teams, you will implement, evaluate and **compare** algorithms for **Collaborative Filtering**.
 
 ### Challenge
-*Collaborative filtering* refers to the process of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).
+*Collaborative filtering* refers to the process of making automatic predictions (*filtering*) about the interests of a user by collecting preferences or taste information from many users (*collaborating*).
 
 For this project, each team is assigned **specific algorithms** from the *Collaborative filtering* literature. You will study the algorithms carefully and implement them, from scratch. **Algorithm assignments will be posted to a piazza post specific to individual groups**.
 
-For submission, you will submit the GitHub repo of your codes, a *testing* report (must be a **reproducible** R notebook or a similar format) on the algorithms in terms of a *side-by-side* comparison of their performance and computational efficiency. For presentation, each team should briefly explain what each algorithm does, how the evaluation was carried out, and what are the main results. 
+For submission, you will submit the GitHub repo of your codes, a *testing* report (must be a **reproducible** R notebook or a similar format) on the algorithms in terms of a *side-by-side* comparison of their performance and computational efficiency. 
+
+For presentation, each team should briefly explain 
+
++ what each algorithm does;
++ how the evaluation was carried out; 
++ and what are the main results. 
 
 All developments need to be carried out in group-shared private repo on [https://www.github.com/TZstatsADS/] with clear project management log, taking advantage of GitHub issues. 
 
 Each week, we will give a tutorial in class and having live discussion and brainstorm sessions. The instruction team will join team discussions during class and online. 
 
-- week 1 [10/30]: Introduction and project description.
-- week 2 [11/6]:  Introduction to Recommender Systems. Q&A.
+- week 1 [Oct 30/31]: Introduction and project description.
+- week 2 [Nov 6/7]:  Introduction to Recommender Systems; Starter codes; Q&A.
+- week 3 [Nov 13/14]: Discussion of assigned algorithms.
 
 #### Evaluation criteria 
 
@@ -32,7 +39,8 @@ Each week, we will give a tutorial in class and having live discussion and brain
 5. [wk2] Based on the outcomes from week 1's reading and brainstorm sessions, continue coding and start evaluation. 
 6. [wk2] Week 2 is the **evaluation** week. 
 7. [wk2] It is ok to separate into two sub-teams, one working on one algorithm, as long as the two teams have the same criteria for evaluating the algorithms. The two sub-teams can also serve as others' validators. 
-8. By using R Notebook to carry out coding and evaluation, your final report can just be adding explanation and comments to your Notebook. 
+8. [wk3] By using R Notebook to carry out coding and evaluation, your final report can just be adding explanation and comments to your Notebook. 
+9. [wk3] Week 3 is the **report writing** week. You want to create clear explanation and illustration of the algorithms by using diagrams (can be borrowed from the papers), case examples, summarizing statistcs, and visualizations of performance statistics.
 
 ### Working together
 - Setup a GitHub project folder from joining the GitHub classroom link with everyone listed as contributors. Everyone clones the project locally via your GitHub desktop and create a local branch. 

README.md

@@ -103,6 +103,7 @@
 #### Weeks 10 (Nov 6/7)
 + Recap on project 4 requirements.
 + [Introduction to Recommender Systems](https://docs.google.com/presentation/d/1bA_uE5D3qnJDaf3DwZ-ZzmR641vVl32oO8pdZEbYafo/edit#slide=id.g643d58c4a4_0_11).
++ Overview of the [starter codes](Projects_StarterCodes/Project4-RecommenderSystem)
 + [Overview of the reference papers](Projects_StarterCodes/Project4-RecommenderSystem/doc/Matrix%20Factorization.pdf).
 
 #### Weeks 11 (Nov 13/14)