Knowledge-Aware Group Representation Learning for Group Recommendation

Abstract

Nowadays, going out and participating in group activities is an indispensable part of human life, and group recommendation systems are needed to provide suggestions. In practice, group recommendation faces serious sparsity issues due to the lack of group-item interaction data, and the key challenge is to aggregate group member preference for group decision making. Conventional group recommendations applied a predefined strategy to aggregate the preferences of group members, which cannot model the group decision making process and do not address the data sparsity problem well. In this paper, we introduce knowledge graph into group recommendation as side information, and propose a novel end-to-end method named knowledge graph-based attentive group recommendation (KGAG) to solve the data sparsity and preference aggregation problems. Specifically, a graph convolution network (GCN) is employed to capture abundant structure information of items and users in knowledge graph to overcome the sparsity problem. Besides, to learn knowledge-aware group representation for inferring the group decision better, we capture the user-item connectivity and user-user connectivity in knowledge graph, and then adopt attention mechanism to learn the influence of each member according to user-user interaction in group and the candidate item, for member preference aggregation. Additionally, the attention mechanism can provide interpretability to group recommendation. Moreover, we extend the margin loss to our KGAG which forces the prediction score of positive item to be a distance larger than that of negative item. Experimental results show the superiority of the proposed KGAG and verify the efficacy of each component of KGAG.