Beyond traditional user recommendation, group recommendation is a new and popular task that provides recommendations for a group of users. Previous works aggregate member preferences in the group to infer group preference, but this often leads to a coarse-grained inference for group preferences limited by users’ individual preferences. To this end, we exploit that user preferences can be inferred and refined by exploring the group preferences that they participated in. These refined preferences offer additional information beyond the original individual preferences, enabling more fine-grained and satisfactory group preference inference. In this work, we propose a novel D ual- G ranularity G raph V ariational A uto- E ncoder framework (DGGVAE) for group recommendation, which jointly reveals group preferences from both coarse granularity and fine granularity to comprehensively learn group preferences. Specifically, we design a Group Preference Extractor module that extracts group preferences from these two granularities: coarse granularity, which is revealed through original member preferences, and fine granularity, which is revealed through refined member preferences. To extract the correlation between groups, a Group Representation Enhancement module is proposed, which enhances group representations by information from the most similar groups. However, the coarse- and fine-grained group preferences contain uncertainty due to the gap between the original and refined member preferences. To better incorporate dual-granularity group preferences, we design granularity-specific graph variational encoders that learn Gaussian variables on the semantic information for each group. Moreover, with the conditional independence assumption, the granularity-specific Gaussian node embeddings are fused according to the generalized product-of-experts (gPoE), where the semantic information in each granularity is weighted based on the estimated uncertainty level. Extensive experiments show the superiority of DGGVAE over various state-of-the-art methods in training efficiency and accuracy on both group and user recommendation tasks.
Xu et al. (2025) studied this question.