Abstract: In recent years, with the development of information technology and the widespread popularity of online social networking, traditional recommender systems have gradually exposed limitations in characterizing complex user needs, thus drawing increasing attention to group recommendation systems. However, most existing group recommendation methods rely on simple aggregation of individual members' preferences, making it difficult to capture implicit consensus in group behavior. To address this problem, this paper proposed IConcen, a hypergraph neural network-based group recommendation model that integrated multi-view information. The model captured group interactions from three complementary perspectives: member level, item level, and group level. It also introduced an adaptive fusion module that dynamically balances the weights of these views to generate highly expressive fused features, effectively improving recommendation performance.Experimental results on the Mafengwo and CAMRa2011 datasets show that the proposed method outperforms mainstream models such as HCR in both group recommendation and user recommendation tasks. Specifically, on the Mafengwo dataset, the proposed method improves HR@5 and NDCG@5 by more than 24.3% and 28.2%, respectively, compared with the S2-HHGR, Agree, and HCR models, verifying its effectiveness and superiority.

Key words: group recommendation, hypergraph, multi-view learning, group preference, recommender system