Multiple hypergraph convolutional network social recommendation using dual contrastive learning

Due to the strong representation capabilities of graph structures in social networks, social relationships are often used to improve recommendation quality. Most existing social recommendation models exploit pairwise relations to mine latent user preferences. However, since user interactions are relatively complex with possibly higher-order relationships, their performance in real-world applications is limited. Furthermore, user behavior data in many practical recommendation scenarios tend to be noisy and sparse, which may lead to suboptimal representation performance. To address this issue, we propose a dual objective contrastive learning multiple hypergraph convolution model for social recommendation (DCMHS). Specifically, our model first constructs hypergraphs with different social relationships. Then, we construct hypergraph encoders to obtain higher-order user representations through hypergraph convolution. Aiming to avoid aggregation loss caused by aggregating user embeddings under different views into one, we construct neighbor identification and semantic identification contrastive learning objectives to iteratively refine the user representation. In addition, we optimize the negative sampling process using the global embedding of items. The results of experiments conducted on real-world datasets demonstrate the effectiveness of the proposed DCMHS, and the ablation study validates the rationality of different components of the model.