Jointly modeling content, social network and ratings for explainable and cold-start recommendation

Model-based approach to collaborative filtering (CF), such as latent factor models, has improved both accuracy and efficiency of predictions on large and sparse dataset. However, most existing methods still face two major problems: (1) the recommendation results derived from user and item vectors of a set of unobserved factors are lack of explanation; (2) cold start users and items out of user-item rating matrix cannot be handled accurately. In this paper, we propose a hybrid method for addressing the problems by incorporating content-based information (i.e, users tags and items keywords) and social information. The main idea behind our method is to build content association based on three factors-user interest in selected tags, tagkeyword relation and item correlation with extracted keywords, and then recommend the items with high similarity in content to users. Two novel methods-neighbor based approach and 3-factor matrix factorization are proposed for building tagkeyword relation matrix and learning user interest vector for selected tags and item correlation vector for extracted keywords. Besides, we introduce a social regularization term to help shape user interest vector. Analysis shows that our method can generate explainable recommendation results with simple descriptions, and experiments on real dataset demonstrate that our method improves recommendation accuracy of state-of-the-art CF models for previous users and items with few ratings, as well as cold start users and items with no rating.