User Interest Propagation and Its Application in Recommender System

Abstract

User interest prediction plays an important role in online services, such as electronic commerce, social network, and online advertising. This information is not always explicitly available for online systems. To identify the interests, existing studies merely focused on modeling the relationships between user generated contents and user interests. However, the interests of a user should not only be inferred by user generated contents, but also the relationships between users which might imply the user interests. In this paper, our goal is to unveil the true interests of users based on user generated contents as well as relationships between users. We built a probabilistic user interests model and proposed a user interests propagation algorithm (UIP) to tackle this problem. A factor graph-based approach is utilized to estimate the distribution of the interests of users. We conducted experiments on real-world datasets to validate the effectiveness of the model. Furthermore, we integrated our UIP algorithm with the classical matrix factorization algorithm to deal with the rating prediction task. Experimental studies confirm the superiority of the proposed approach.