Multiple Hypergraph Learning for Ephemeral Group Recommendation

Abstract

Ephemeral Group Recommendation (EGR) refers to recommending items for a temporarily existing group, where the ephemeral group has little or no historical interactions with items while each group member has his/her own interaction history. We note that EGR not only faces the challenge of extremely sparse or nonexistent group-item interactions and also has its own special needs. EGR needs to seek the common preferences of the members instead of maximizing the personalized needs of individuals. In particular, group preferences may not necessarily be related to the timeliness and intensity of the member’s individual behavior and preferences. Following this line of thought, we propose an EGR model named HL4EGR. Specifically, we adopt hypergraphs to model complex relationships among users, items, and groups, during which we weaken the timeliness and intensity of user behavior and preferences and augment training data by discovering implicit and explicit group-group similarities. Moreover, we design a cross-hypergraph contrastive learning strategy to align embeddings for the same group in different hypergraphs, which enables group preferences to reflect the common preferences of group members comprehensively. We conduct extensive experiments on three real-world datasets, and the experimental results show that our model HL4EGR outperforms state-of-the-art models.