User´s Interests-Based Movie Recommendation in Heterogeneous Network

For large data set of movies and various users´ interests, recommender systems should pay attention to time consumption and personal interest. However, existing information filtering techniques rarely research users´ interests in movies. In order to implement personalized movie recommendation effectively, we propose user´s interests-based movie recommendation in heterogeneous network (IMRHN). First, we divide the heterogeneous network consisted of movies, movie genres and users into several sub-networks based on movie genres, and then mine active users´ interest probability on every movie genre through other users´ influence. At last, we recommend higher marked movies for each certain eligible movie genre that attracts more active user´s interest. Experimental analysis suggests that IMRHN can achieve personalized recommendation and perform better in time consumption than other several algorithms.