Item Group Recommendation: A Method Based on Game Theory

Limeng Zhang, Rui Zhou, Haixin Jiang, Hua Wang, Yanchun Zhang

Abstract: In this paper, we focus on recommending an item set to multiple users. Group recommender systems are designed to deal with the issue of recommending items for a user group. However, in some scenarios such as gift set promotion (different items are packed together as a gift set), album promotion, we need to focus on consumers' preference to multiple items rather than to some specific item. To deal with this issue, we pioneer a Nash equilibrium based Item Group Recommendation approach (NIGR). Specifically, we evaluate each consumer's preference to an item group in two perspectives, interest part from the customer herself and social affection from her friends. Then, we model the recommending process as a game to achieve Nash equilibrium. Finally, we demonstrate the effectiveness of our approach with extensive experiments.