DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation

Group recommendation aims to suggest items to a group of users that are suitable for the group. Although some existing powerful deep learning models have achieved improved performance, various aspects remain unexplored: (1) Most existing models using contrastive learning tend to rely on high-quality data augmentation which requires precise contrastive view generation; (2) There is multifaceted natural noise in group recommendation, and additional noise is introduced during data augmentation; (3) Most existing hypergraph neural network-based models over-entangle the information of members and items, ignoring their unique characteristics. In light of this, we propose a highly effective Disentangled Hypergraph Masked Auto Encoder-enhanced method for group recommendation (DHMAE), combining a disentangled hypergraph neural network with a graph masked autoencoder. This approach creates self-supervised signals without data augmentation by masking the features of some nodes and hyperedges and then reconstructing them. For the noise problem, we design a masking strategy that relies on pre-computed degree-sensitive probabilities for the process of masking features. Furthermore, we propose a disentangled hypergraph neural network for group recommendation scenarios to extract common messages of members and items and disentangle them during the convolution process. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art models and effectively addresses the noise issue.