Matrix co-factorization for recommendation with rich side information and implicit feedback

Most recommender systems focus on the areas of leisure activities. As the Web evolves into omnipresent utility, recommender systems penetrate more serious applications such as those in online scientific communities. In this paper, we investigate the task of recommendation in online scientific communities which exhibit two characteristics: 1) there exists very rich information about users and items; 2) The users in the scientific communities tend not to give explicit ratings to the resources, even though they have clear preference in their minds. To address the above two characteristics, we propose matrix factorization techniques to incorporate rich user and item information into recommendation with implicit feedback. Specifically, the user information matrix is decomposed into a shared subspace with the implicit feedback matrix, and so does the item information matrix. In other words, the subspaces between multiple related matrices are jointly learned by sharing information between the matrices. The experiments on the testbed from an online scientific community (i.e., Nanohub) show that the proposed method can effectively improve the recommendation performance.