Group recommendation aims to recommend tailored items to groups of users, where the key challenge is modeling a consensus that reflects member preferences. Although several existing deep learning models have achieved performance improvements, they still fail to capture consensus in various aspects: (1) Capturing consensus in small-group (2~5 members) recommendation systems, which align more closely with real-world scenarios, remains a significant challenge; (2) Most existing models significantly enhance the overall group performance but struggle with balancing individual and group performance. To address these issues, we propose Capturing Consensus with Contrastive Learning in Group Recommendation (C$^3$), which focuses on exploring the consensus behind group decision-making. A Transformer encoder is used to learn both group and user representations, and contrastive learning mitigates overfitting for users with many interactions, yielding more robust group representations. Experiments on four public datasets demonstrate that C$^3$ significantly outperforms state-of-the-art baselines in both user and group recommendation tasks.

翻译：群体推荐旨在向用户群体推荐定制化的项目，其核心挑战在于建模能够反映成员偏好的共识。尽管现有的若干深度学习模型已取得性能提升，但它们仍难以在多方面有效捕捉共识：（1）在小规模群体（2~5名成员）推荐系统中捕捉共识仍面临重大挑战，而此类场景更贴近现实应用；（2）大多数现有模型虽能显著提升整体群体性能，却难以平衡个体与群体性能。为解决这些问题，我们提出基于对比学习的群体推荐共识捕捉方法（C$^3$），其核心在于探索群体决策背后的共识机制。我们采用Transformer编码器学习群体与用户的表示，并通过对比学习缓解高交互用户的过拟合问题，从而获得更稳健的群体表示。在四个公开数据集上的实验表明，C$^3$在用户推荐与群体推荐任务中均显著优于当前最先进的基线方法。