Preference-Based Graphic Models for Collaborative Filtering

Abstract

Collaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previous research has studied several probabilistic graphic models for collaborative filtering with promising results. However, while these models have succeeded in capturing the similarity among users and items, none of them has considered the fact that users with similar interests in items can have very different rating patterns; some users tend to assign a higher rating to all items than other users. In this paper, we propose and study two new graphic models that address the distinction between user preferences and ratings. In one model, called the decoupled model, we introduce two different variables to decouple a user's preferences from hislher ratings. In the other, called the preference model, we model the orderings of items preferred by a user, rather than the user's numerical ratings of items. Empirical study over two datasets of movie ratings shows that, due to its appropriate modeling of the distinction between user preferences and ratings, the proposed decoupled model significantly outperforms all the five existing approaches that we compared with. The preference model, however, performs much worse than the decoupled model, suggesting that while explicit modeling of the underlying user preferences is very important for collaborative filtering, we can not afford ignoring the rating information completely.