Learning Preferences of New Users in Recommender Systems: An Information Theoretic Approach

Liveblogging from WebKDD 08

Learning Preferences of New Users in Recommender Systems: An Information Theoretic Approach
Al M. Rashid, George Karypis, and John Riedl

[Update: This was the winner for the best paper award]

This paper is addresses the cold start problem in collaborative filtering, i.e. for new users since the system does not know of the user's preferences, the system cannot recommend items. This paper looks at a few different heuristics:

• Popularity of an item. The system picks most popular items and presents the new user with these. If the user rates these items, it constitutes the initial profile. The interesting point they note is that a good strategy is to pick popular items that have a varied rating distribution. The downside is unpopular items are harder to predict.
• Entropy: identify informative items. Downside is that entropy does not pick the right item when the ratings are few.
• Entropy0: treat missing ratings as separate rating items. However, this favors popular items more.
• HELF: combines entropy with popularity.
• ICGN: Information gain through clustered neighbors. Use ratings so far to determine what is informative next.

Evaluation was done on the MovieLens recommendation system.Overall ICGN and Entropy0 perform the best.