Contrastive Variational Group Recommendation With Data-Agnostic Augmentation

Group recommendation aims to recommend desired items for a group of users. Existing methods mainly adopt deterministic networks to represent groups as fixed-point vectors, assuming their preferences be highly close to these vectors in interest space. However, each group tends to have various interests, which cannot be fully captured by fixed-point vectors and thus calls for probabilistic modeling of interests as density instead. Although this can be supported by Variational AutoEncoder (VAE), interaction data in group recommendation are highly sparse and insufficient for VAE model training, resulting in high risks of posterior collapse and deficiency in personalization. To this end, this paper proposes a contrastive variational learning model boosted by variational model augmentation and an easyto-hard paradigm. Specifically, VAE with tailored attention is first employed to represent group preferences as variational vectors for probabilistic preference modeling. Additionally, we conduct data-agnostic augmentation via learnable variational dropout, which removes redundant or irrelevant neurons in VAE to generate meaningful augmented views adequately for contrastive learning in spite of data sparsity. Difficulty-aware negative sampling is further applied to generate high-quality negative samples adapting to varying requirements of task difficulty according to the training process. Finally, we utilize density-based variational alignment to guide the optimization process of contrastive learning. Experiments on four real-world datasets are conducted to demonstrate the significant performance improvements of our model compared with SOTA methods for group recommendation.