A Hybrid Movie Recommendation Method Based on Social Similarity and Item Attributes

摘要

With the increasing demand for personalized recommendation, traditional collaborative filtering cannot satisfy users' needs. Social behaviors such as tags, comments and likes are becoming more and more popular among the recommender system users, and are attracting the attentions of the researchers in this domain. The behavior characteristics can be integrated with traditional interest community and some content features. In this paper, we put forward a hybrid recommendation approach that combines social behaviors, the genres of movies and existing collaborative filtering algorithms to perform movie recommendation. The experiments with MovieLens dataset show the advantage of our proposed method comparing to the benchmark method in terms of recommendation accuracy.