Exploring User Influence for Topical Group Recommendation

With the development of social networks, many recommendation systems recommend items to a group of users, known as group recommendation. However, it will not be an appropriate recommendation without user topical influence analysis. We proposed a new group recommendation based on user topical influence analysis. We firstly construct several topical sub-groups depending on topics. Then we analyze user topical influence in subgroup, including user influence on specific topic and the topical sub-group. Besides, four user-factors are introduced to calculate the user social influence on topical sub-groups more accurately. Based on user topical influence analysis, we present our topical group recommendation algorithm, which calculates the predicted rating value for sub-group by aggregating weighted ratings of all users in the subgroup. The experimental results show convincingly that our proposed method can improve the group recommendation quality.