Abstract
Recommender systems aim to provide users with preferred items to tackle the information overload problem in the Web era. Social relations, item connections, and user-generated reviews on items contain abundant potential information. By combining matrix factorization with latent Dirichlet allocation, we integrate ratings, reviews, user similarity and item similarity in recommender systems. The experimental result on a real-world dataset proves that both item connection and user connection contain useful sources for recommendation, and our model can effectively improve recommendation quality.