Multi-View Collaborative Training and Self-Supervised Learning for Group Recommendation

Recommendation systems offer an effective solution to information overload, finding widespread application across e-commerce, news platforms, and beyond. By analyzing interaction histories, these systems automatically filter and recommend items that are most likely to resonate with users. Recently, with the swift advancement of social networking, group recommendation has emerged as a compelling research area, enabling personalized recommendations for groups of users. Unlike individual recommendation, group recommendation must consider both individual preferences and group dynamics, thereby enhancing decision-making efficiency for groups. One of the key challenges facing recommendation algorithms is data sparsity, a limitation that is even more severe in group recommendation than in traditional recommendation tasks. While various group recommendation methods attempt to address this issue, many of them still rely on single-view modeling or fail to sufficiently account for individual user preferences within a group, limiting their effectiveness. This paper addresses the data sparsity issue to improve group recommendation performance, overcoming the limitations of overlooking individual user recommendation tasks and depending on single-view modeling. We propose MCSS (multi-view collaborative training and self-supervised learning), a novel framework that harnesses both multi-view collaborative training and self-supervised learning specifically for group recommendations. By incorporating both group and individual recommendation tasks, MCSS leverages graph convolution and attention mechanisms to generate three sets of embeddings, enhancing the model's representational power. Additionally, we design self-supervised auxiliary tasks to maximize the data utility, further enhancing performance. Through multi-task joint training, the model generates refined recommendation lists tailored to each group and individual user. Extensive validation and comparison demonstrate the method's robustness and effectiveness, underscoring the potential of MCSS to advance state-of-the-art group recommendation.