Preference-based Graphic Models for Collaborative Filtering

TL;DR

This paper introduces two new probabilistic graphic models for collaborative filtering that distinguish between user preferences and ratings, demonstrating that explicit preference modeling enhances recommendation accuracy.

Contribution

The paper proposes two novel models, the decoupled and preference models, to better capture user preferences separate from ratings, improving collaborative filtering performance.

Findings

Decoupled model outperforms existing approaches significantly.

Modeling user preferences explicitly is crucial for effective filtering.

Preference model alone is less successful than the decoupled model.

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 in one way or the other, 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 of 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 users preferences FROM his ratings. IN the other, called the preference model, we model the orderings OF items preferred BY a USER, rather than the USERs numerical ratings of items. Empirical study over two datasets of movie ratings shows that appropriate modeling of the distinction between user preferences and ratings improves the performance substantially and consistently. Specifically, the proposed decoupled model outperforms all five existing approaches that we compare with significantly, but the preference model is not very successful. These results suggest that explicit modeling of the underlying user preferences is very important for collaborative filtering, but we can not afford ignoring the rating information completely.