Social Recommendation Incorporating Topic Mining and Social Trust Analysis

We study the problem of social recommendation incorporating topic mining and social trust analysis. Different from other works related to social recommendation, we merge topic mining and social trust analysis techniques into recommender systems for finding topics from the tags of the items and estimating the topic-specific social trust. We propose a probabilistic matrix factorization (TTMF) algorithm and try to enhance the recommendation accuracy by utilizing the estimated topic-specific social trust relations. Moreover, TTMF is also convenient to solve the item cold start problem by inferring the feature (topic) of new items from their tags. Experiments are conducted on three different data sets. The results validate the effectiveness of our method for improving recommendation performance and its applicability to solve the cold start problem.