Semantic and Structural View Fusion Modeling for Social Recommendation

Existing studies have shown that user-item interaction data and social relation data can be jointly used for enhancing the performance of social recommendation. However, limited research has a focus on investigating how to deeply exploit different views of social interaction structures and rating behavior differences for further improving social recommendation. To this end, in this paper, we propose to integrate information from both semantic and structural views for social recommendation. Specifically, we first design a collective intelligence-based strategy to reveal high-quality implicit relations for both users and items. Then, by reformulating all available nodes and relations as a heterogeneous graph, we define multiple semantic metapaths to capture diverse preferences for comprehensive user and item representations. While various metapaths enlarge the representation capacity of users and items, they also introduce noise and irrelevant information. We recall that, for the user-item interaction graph, different structure sizes (e.g., local and global structures) provide diverse and complementary information for recommendation. Motivated by this, we propose a semantic and structural view fusion framework for social recommendation (S4Rec), which consists of a deep graph model and a wide attentive SVD (Singular Value Decomposition) model for rating prediction by taking the local and global structure as input and aggregating messages along the predefined metapaths. Finally, the two predicted results are adaptively fused to achieve the final both accurate and stable prediction. In addition, we treat the user's rating behavior difference as the relative position difference problem in the embedding space, and model it with TransH to improve the generalization ability of the main rating model. Extensive experiments on three open datasets demonstrate the superiority of our framework compared with state-of-the-art methods. Particularly, our model outperforms other baselines under different sparsity conditions, further validating the effectiveness on cold-start users. We release the source code at
https://github.com/lcwy220/Social-Recommendation
.