Collaborative Topic Regression with social trust ensemble for recommendation in social media systems

Social media systems provide ever-growing huge volumes of information for dissemination and communication among communities of users, while recommender systems aim to mitigate information overload by filtering and providing users the most attractive and relevant items from information-sea. This paper aims at providing compound recommendation engine for social media systems, and focuses on exploiting multi-sourced information (e.g. social networks, item contents and user feedbacks) to predict the ratings of users to items and make recommendations. For this, we suppose the users' decisions on adopting item are affected both by their tastes and the favors of trusted friends, and extend Collaborative Topic Regression to jointly incorporates social trust ensemble, topic modeling and probabilistic matrix factorization. We propose corresponding approaches to learning the latent factors both of users and items, as well as additional parameters to be estimated. Empirical experiments on Lastfm and Delicious datasets show that our model is better and more robust than the state-of-the-art methods on making recommendations in term of accuracy. Experiments results also reveal some useful findings to enlighten the development of recommender systems in social media.