Social-Aware Movie Recommendation via Multimodal Network Learning

With the rapid development of Internet movie industry, social-aware movie recommendation systems (SMRs) have become a popular online web service that provide relevant movie recommendations to users. In this effort, many existing movie recommendation approaches learn a user ranking model from user feedback with respect to the movie's content. Unfortunately, this approach suffers from the sparsity problem inherent in SMR data. In the present work, we address the sparsity problem by learning a multimodal network representation for ranking movie recommendations. We develop a heterogeneous SMR network for movie recommendation that exploits the textual description and movie-poster image of each movie, as well as user ratings and social relationships. With this multimodal data, we then present a heterogeneous information network learning framework called SMR-multimodal network representation learning (MNRL) for movie recommendation. To learn a ranking metric from the heterogeneous information network we also developed a multimodal neural network model. We evaluated this model on a large-scale dataset from a real world SMR Web site, and we find that SMR-MNRL achieves better performance than other state-of-the-art solutions to the problem.