Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems
arXiv:1206.4684
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.
ICML2012
Cited by in corpus (8)
- Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms
- TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation
- Social Collaborative Retrieval
- A Survey on Bayesian Deep Learning
- A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
- Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model
- Deep Social Collaborative Filtering
- Bayesian Matrix Completion for Hypothesis Testing