COM: a generative model for group recommendation

著者: Quan Yuan, Gao Cong, Chin-Yew Lin - ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2014 被引用: 199

With the rapid development of online social networks, a growing number of people are willing to share their group activities, e.g., having dinners with colleagues, and watching movies with spouses. This motivates the studies on group recommendation, which aims to recommend items for a group of users. Group recommendation is a challenging problem because different group members have dif-ferent preferences, and how to make a trade-off among their pref-erences for recommendation is still an open problem. In this paper, we propose a probabilistic model named COM (COnsensus Model) to model the generative process of group ac-tivities, and make group recommendations. Intuitively, users in a group may have different influences, and those who are expert in topics relevant to the group are usually more influential. In addi-tion, users in a group may behave differently as group members from as individuals. COM is designed based on these intuitions, and is able to incorporate both users ’ selection history and personal considerations of content factors. When making recommendations, COM estimates the preference of a group to an item by aggregat-ing the preferences of the group members with different weights. We conduct extensive experiments on four datasets, and the results show that the proposed model is effective in making group recom-mendations, and outperforms baseline methods significantly.