Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' preferences via aggregating diverse members' interests. Actually, groups' ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions. To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling.

翻译：由于群体活动在日常生活中已变得非常普遍，为群组用户生成推荐（即群组推荐任务）的需求日益迫切。现有群组推荐方法通常通过聚合不同成员的兴趣来推断群组偏好。实际上，群组的最终选择涉及成员间的妥协，并最终达成一致。然而，现有基于个体信息聚合的方法缺乏整体性的群组层面考虑，未能捕获共识信息。此外，其特定的聚合策略要么计算成本过高，要么过于粗粒度而难以做出精确预测。为解决上述局限，本文聚焦于探索群组行为数据背后的共识。为全面捕获群组共识，我们创新性地设计了三种不同视角，通过提供互补信息实现多视角学习，包括成员级聚合、物品级偏好和群组级固有偏好。为整合并平衡多视角信息，进一步提出了自适应融合组件。在成员级聚合方面，与现有线性或注意力策略不同，我们设计了一种新颖的超图神经网络，能够通过高效的超图卷积运算生成富有表现力的成员级聚合。我们在两个真实数据集上评估了ConsRec模型，实验结果表明我们的模型优于现有先进方法。广泛的案例研究也验证了共识建模的有效性。