The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems lever age 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 tum, the probability that he or she will like new items. PD retains some of the ad vantages of traditional similarity-weighting tech niques in that all data is brought to bear on each prediction and new data can be added easily and incrementally. Additionally, PD has a mean ingful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie database of movie ratings, and on user profile data collected from the CiteSeer digital library of Computer Science research papers. The prob abilistic framework naturally supports a variety of descriptive measurements-in particular, we consider the applicability of a value of informa tion (VOl) computation. The goal of collaborative filtering (CF) is to predict the preferences of one user, referred to as the active user, based on the preferences of a group of users. For example, given the active user's ratings for several movies and a database of other users' ratings, the system predicts how the active user would rate unseen movies. The key idea is that the active user will prefer those items that like-minded people prefer, or even that dissimilar people don't prefer. The effectiveness of any CF algorithm is ultimately predi cated on the underlying assumption that human preferences are correlated-if they were not, then informed prediction would not be possible. There does not seem to be a sin gle, obvious way to predict preferences, nor to evaluate effectiveness, and many different algorithms and evalua tion criteria have been proposed and tested. Most compar isons to date have been empirical or qualitative in nature
Another recent paper uses a probabilistic method called "personality diagnosis" (PD) REF , and gives better accuracy than Pearson.
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
ArXiv
ArXiv
Computer Science