Compsci 101, Fall 2012, Recommender Systems

Collaborative filtering and content-based filtering are two kinds of recommender systems that provide users with information to help them find and choose anything from books, to movies, to restaurants, to courses based on their own preferences compared to the preferences of others.

In 2009 Netflix awarded one million dollars to a group that had developed a better-recommender system than the Netflix, in-house system. This NY Times Magazine article describes the competition, the winning teams, and how the movie Napolean Dynamite caused problems for the algorithms and ranking/rating systems developed by contest participants.

In this assignment, adapated from a Nifty Assignment developed by Michelle Craig, you'll develop a program to test three different algorithms for recommending items based on the responses made by others. You'll be practicing reading data from files, using Python dictionaries and lists, and sorting data to find good matches.

The assignment comes in two conceptual parts:

• Reading data stored in files and converting the data into a common format

• Using the data (stored in the common format) to make recommendations for either every students/rater or for a particular student as described below.

We're providing two sources of data initially, but we'll solicit ratings from Duke students that you'll process as well. Sometimes ratings are stored in a single file, sometimes in more than one file. You'll need to write a separate Python module to deal with each data source, then use what these modules return to develop ratings.