Multi-view contrastive learning with virtual social group influence for social recommendation

Social recommendation systems leverage user–item interaction and user–user social network data to model user preferences and provide recommendations. Previous research has shown that capturing the influence of high-order neighbors and social groups in social networks can help model user preferences. However, challenges still exist in identifying important high-order neighbors from many candidates and modeling social group influence without direct information on social groups. This paper proposes a Multi-view Contrastive Learning with Social Group Influence (MCLSGI) method to address these challenges. Our approach uses a graph walk method to identify users’ virtual social groups and a GNN-based framework to model user preferences in different views, capturing the influence of direct and high-order neighbors as well as social groups. We also adopt multi-view contrastive learning to fuse users’ preferences in different views. We conducted experiments on two real-world datasets (Ciao and Epinions) to validate our method's effectiveness. Compared to the best baseline, we improved by 1.39% and 1.21% with MAE, and 1.79% and 0.96% with RMSE.