A time-sensitive personalized recommendation method based on probabilistic matrix factorization technique

Personalized recommender systems are the most effective way to solve the problem of information overload. The majority of traditional personalized recommender systems employ the collaborative filtering (CF) approach. CF leverages users' behaviors to infer a target user's preference for a particular item, while ignores the fact that users interact with the system within a particular context, such as a particular time interval or location. In this paper, we propose a novel time-sensitive personalized recommendation method called TSPR for movie recommendation. Specifically, we first define and construct a new user-context rating matrix based on the original user-movie rating matrix and then propose a novel context-dependent similarity measurement by mining the implicit relationship among users from the user-context rating matrix. Further, we build a context-dependent similarity matrix based on the context-dependent similarity measurement. Finally, we incorporate the context-dependent similarity matrix into the probabilistic matrix factorization model. The experimental results show that TSPR performs much better than the state-of-the-art recommendation methods.