An interest propagation based movie recommendation method for social tagging system

Collaborative tags labeled by users in social tagging systems contain rich information about individual preference and resource content, which can further improve the performance of personalized recommender system. In this paper, a hybrid method that combines the collaborative filtering with graph-based interest propagation is proposed for movie recommendation. In the proposed method, both user and movie profiles are constructed by tags. The top-k similar users are collected by user-user interest which is calculated by the user profiles, and then a user-movie bipartite graph is constructed according to the top-k users and candidate movies. We utilize the user and movie profiles to calculate user-movie interest, and the interest is propagated in the graph until converged or the max iterations are reached. Lastly, the top-n movies in the graph are recommended to the initial user. Experimental results on MovieLens dataset demonstrate that our proposed method can achieve better performance than several baselines for the movie recommendation problem.