Probabilistic Models for Contextual Agreement in Preferences

Abstract

The long-tail theory for consumer demand implies the need for more accurate personalization technologies to target items to the users who most desire them. A key tenet of personalization is the capacity to model user preferences. Most of the previous work on recommendation and personalization has focused primarily on individual preferences. While some focus on shared preferences between pairs of users, they assume that the same similarity value applies to all items. Here we investigate the notion of “context,” hypothesizing that while two users may agree on their preferences on some items, they may also disagree on other items. To model this, we design probabilistic models for the generation of rating differences between pairs of users across different items. Since this model also involves the estimation of rating differences on unseen items for the purpose of prediction, we further conduct a systematic analysis of matrix factorization and tensor factorization methods in this estimation, and propose a factorization model with a novel objective function of minimizing error in rating differences. Experiments on several real-life rating datasets show that our proposed model consistently yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.