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Collaborative Topic Regression Based on the Social Network and Sequential Behaviors

Social Network has become a very useful platform for users to share information and make friends with each other. In our daily life, we would like to take friends' advice when we choose products through the Internet. The sequential behaviors also play an important role in making recommendation, we can make use of the sequential factors to mine the relation between users. In order to take advantage of different factors when predicting ratings and enhancing the recommendation accuracy, we propose a novel hierarchical Bayesian model called N-CTR which combines topic model with probabilistic matrix factorization. Our model incorporates not only topic model to mine the latent topic between items and their tags, but also matrix factorization which handles ratings, social network and sequential behaviors. We have conducted experiments on data set hetcrec-2011-Lastfm. Compare with other recommendation algorithms, our method can effectively enhance the recommendation accuracy.