Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called personality diagnosis (PD). Given a user's preferences for some items, we compute the probability that he or she is of the same "personality type" as other users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all data is brought to bear on each prediction and new data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be...

• Type: Conference
• Year: 2000
• Where: UAI
• Authors: David M. Pennock, Eric Horvitz, Steve Lawrence, C. Lee Giles
• Keywords: Meaningful Probabilistic Interpretation, Method Called Personality, Such Systems Leverage, UAI 2000, UAI 2008