MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation
Xu Guo, Tong Zhang, Fuyun Wang, Xudong Wang, Xiaoya Zhang, Xin Liu, Zhen Cui
Abstract
The burgeoning presence of multimodal content-sharing platforms propels the development of personalized recommender systems. Previous works usually suffer from data sparsity and cold-start problems and may fail to adequately explore semantic user–product associations from multimodal data. To address these issues, we propose a novel Multi-Modal Hypergraph Contrastive Learning (MMHCL) framework for user recommendation. For a comprehensive information exploration from user–product relations, we construct two hypergraphs, i.e., a user-to-user (u2u) hypergraph and an item-to-item (i2i) hypergraph, to mine shared preferences among users and intricate multimodal semantic resemblance among items, respectively. This process yields denser second-order semantics that are fused with first-order user–item interaction as complementary to alleviate the data sparsity issue. Then, we design a contrastive feature enhancement paradigm by applying synergistic contrastive learning. By maximizing/minimizing the mutual information between second-order (e.g., shared preference pattern for users) and first-order (information of selected items for users) embeddings of the same/different users and items, the feature distinguishability can be effectively enhanced. Compared with using sparse primary user–item interaction only, our MMHCL obtains denser second-order hypergraphs and excavates more abundant shared attributes to explore the user–product associations, which to a certain extent alleviates the problems of data sparsity and cold-start. Extensive experiments have comprehensively demonstrated the effectiveness of our method. Our code is publicly available at https://github.com/Xu107/MMHCL .