Multi-view Attentive Variational Learning for Group Recommendation

Group recommendation aims to recommend desired items for a group of users. Due to the sparsity of group-item interactions, existing methods mainly model group preferences by aggregating member-level preference. However, they not only ignore possible user interest drift in specific groups, but also adopt deterministic models to represent group preferences using fixed-points, which are weak in characterizing uncertain group preferences. To this end, following the paradigm of variational learning, this paper proposes a multi-view attentive variational preference aggregation network called GroupAV for group rec-ommendation, so as to conduct user/group preference modeling and aggregation in a density-based manner. Specifically, we first adopt Variational AutoEncoder (VAE) to capture member-level preferences by variational vectors as density. To address user interest drift in groups, a variational preference adapter module is designed to learn group-contextualized preferences via rational transformation in variational space. Next, attentive variational aggregation networks are carefully designed for group-level preference aggregation in two different views (i.e., group-interactions and member-consensus views). Besides, we apply contrastive learning and gating fusion to optimize the multi-view learning process for the final group preference modeling of Mixture-of-Gaussian distribution. Finally, we conduct experiments on real-world datasets and demonstrate GroupAV's significant performance improvements compared to state-of-the-art group recommendation methods.