Movie Collaborative Filtering with Multiplex Implicit Feedbacks

Abstract Movie recommender systems have been widely used in a variety of online networking platforms to give users reasonable advice from a large number of choices. As a representative method of movie recommendation, collaborative filtering uses explicit and implicit feedbacks to mine users’ preferences. The use of implicit features can help to improve the accuracy of movie collaborative filtering. However, multiplex implicit feedbacks have not been investigated and utilized comprehensively. In this paper, we analyze different kinds of implicit feedbacks in movie recommendation, including user similarities for movie tastes, rated records of each movie and positive attitude of users, and incorporate these feedbacks for collaborative filtering. User relationships are extracted according to user similarities. We propose a recommendation method with multiplex implicit feedbacks (RMIF), which factorizes both the explicit rating matrix and implicit attitude matrix. To demonstrate the effectiveness of our method, we conduct extensive experiments on two real datasets. Experiment results prove that RMIF significantly outperforms state-of-the-art models in terms of accuracy. Among different kinds of implicit feedbacks, positive attitude has the most important role in movie collaborative filtering.