Dynamic Group Recommendation with Modified Collaborative Filtering and Temporal Factor

Jinpeng Chen, Yu Liu and Deyi Li

School of Computer Science and Engineering, BeiHang University, China

Abstract: Group recommendation, which provides a group of users with information items, has become increasingly im-portant in both the workspace and people’s social activities. Because users change their preferences or interests over time, the dynamics and diversity of group members is a challenging problem for group recommendation. In this article, we introduce a novel group recommendation method via fusing the modified collaborative filtering methodology with the temporal factor in order to, solve the dynamics problem. Meanwhile, we also put forward a new method of eliminating sparse problem so as to improve the accuracy of recommendation. We have tested our method in the music recommendation domain. Experimental results indicate the proposed group recommender method provides better performance than an original method and gRecs. The result of efficiency and scalability test also shows our method is usable.

Keywords: Recommender systems, group recommendation, collaborative filtering, temporal factor, sparsity, dynamics.

Received August 31, 2013; accepted April 20, 2014