Due Date: Wed, May 23, 2007 at the start of class.

1. Read the paper Empirical Analysis of Predictive Algorithms for Collaborative Filtering. You need to read up to Section 2.1, and are encouraged to read further if you have time.

2. The dataset we will be using is a subset of the movie ratings data from the Netflix Prize. You can download it here (link deactivated). It  contains a training set, a test set, a movies file, a dataset description file, and a README file. The training and test sets are both subsets of the Netflix training data.You will use the ratings provided in the training set to predict those in the test set. You will compare your predictions with the actual ratings provided in the test set. The evaluation metrics you need to measure are the Mean Absolute Error and the Root Mean Squared Error. The dataset description file further describes the dataset, and will help you get started. The README file is from the original set of Netflix files, and has been included to comply with the terms of use for this data.

3. Implement the collaborative filtering algorithm described in Section 2.1 of the paper (Equations 1 and 2; ignore Section 2.1.2) for making the predictions. You may program in C, C++, Java, or C#. If you'd like to use another language, ask Bhushan first.

4. Try to improve the basic algorithm you implemented, for example using one or more of the enhancements described in the paper, or enhancements of your own design.

5. (10% Extra Credit) Add yourself as a new user to the training set. To do this, you will need to create a new user ID for yourself. Select some movies that you have seen among those in the training set, and add your ratings for them to the training set. Extend your system to output predictions for the movies you haven't rated, ranked in order of decreasing ratings. Do you agree with the predictions of your system? Check out some of the top ranked movies that you haven't seen (but only after you have finished work on the project).

6. Turn in the following:
   • Your code, and reasonable documentation for it (i.e., enough for us to understand how it is organized and how to use it). Please place this documentation in a file named README.

   • A report of at most 3 pages (letter size, 1in margins, 12pt font) describing the results you obtained with this algorithm, any modifications you may have tried, and how they affected the results. You can also include what you got upon adding yourself as a new user.

   Turn-in procedure: Please email a zip file with your code and documentation to Bhushan before class on May 23. Turn in a hard copy of your report if you can. Otherwise, just include it in the zip file.
   

7. We may ask you to do a demo of your system and/or an oral discussion.

Good luck, and have fun!