近年来，随着信息技术的发展和网络社交的广泛普及，传统推荐系统在刻画复杂用户需求方面逐渐暴露出局限性，从而引发了对群体推荐系统的日益关注。然而，现有群组推荐方法多依赖于对个体成员偏好的简单汇聚，难以捕捉群体行为中的隐含共识信息。本文针对该问题，提出一种融合多视角信息的超图神经网络群组推荐模型IConcen。该模型从成员级、物品级和群体级三种互补视角对群组交互行为进行建模，并引入自适应融合模块动态协调各视图权重，生成高表达性融合特征，有效提升推荐性能。在Mafengwo和CAMRa2011数据集上的实验结果表明，该方法在群组推荐与用户推荐任务中的性能均优于HCR等主流模型。其中，在Mafengwo数据集上，相较于S2-HHGR、Agree和HCR模型，HR@5和NDCG@5指标分别提升24.3%和28.2%以上，验证了所提方法的有效性与优越性。
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.
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