C$^3$: Capturing Consensus with Contrastive Learning in Group Recommendation

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 deep learning models have improved performance, they still struggle to capture consensus in two important aspects: (1) capturing consensus in small groups (2~5 members), which better reflect real-world scenarios; and (2) balancing individual and group performance while improving overall group accuracy. To address these issues, we propose C$^3$(Capturing Consensus with Contrastive Learning) for group recommendation, which explicitly explores the consensus underlying group decision-making. C$^3$ uses a Transformer encoder to learn both user and group representations, and employs contrastive learning to mitigate overfitting for users with many interactions, resulting in more robust group representations. Experiments on four public datasets show that C$^3$ consistently outperforms state-of-the-art baselines in both user and group recommendation tasks.