Exploring user topic influence for group recommendation on learning resources

With the rapid development of online social networking services, the recommendation systems are facing new challenges in recommending resources to a target group of users. How to make a trade-off between group's preference and influencer's impact is one of important problems, especially in the group recommendation on learning resources. In this paper, we propose a User Topic Influence (UTI) model, which fully exploits user topic influence together with group's preference and item content for group recommendation on learning resources. Based on the UTI model, we mine the topic influence and group's preference through statistical inference, then we develop a parameter learning algorithm through Expectation Maximization (EM) algorithm. In addition, we propose a group recommendation algorithm with the consideration of group's preference and influencers' impact. The experimental results demonstrate that our proposed group recommendation algorithm performs better than other five alternatives.