Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems

TL;DR

This paper introduces a hierarchical Bayesian model that combines topic modeling and social matrix factorization to improve recommendation accuracy by leveraging social network data.

Contribution

It presents a novel joint model that automatically infers latent topics and social influence, enhancing collaborative filtering for recommendation systems.

Findings

Social influence impacts user decisions more than personal taste.

The proposed model outperforms state-of-the-art recommendation algorithms.

Insights into potential information leaks in social-based recommendations.

Abstract

Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user's social circle in their decision process. In this paper, we are interested in examining the effectiveness of social network information to predict the user's ratings of items. We propose a novel hierarchical Bayesian model which jointly incorporates topic modeling and probabilistic matrix factorization of social networks. A major advantage of our model is to automatically infer useful latent topics and social information as well as their importance to collaborative filtering from the training data. Empirical experiments on two large-scale datasets show that our algorithm provides a more effective recommendation system than the state-of-the art approaches. Our results reveal interesting insight that the social circles have more influence on people's decisions about the usefulness of information (e.g., bookmarking preference on Delicious) than personal taste (e.g., music preference on Lastfm). We also examine and discuss solutions on potential information leak in many recommendation systems that utilize social information.