Evolving Graph Contrastive Learning for Socially-aware Recommendation

摘要: Social recommendations play a crucial role in providing personalized services to users by leveraging social relationships and user sessions. Despite recent advancements, it still faces challenges in dealing with social inconsistency and the loss of critical semantic information in user-service interactions. To overcome these problems, an Evolving Graph Contrastive Learning for Socially-aware Recommendation (EGCLSR) model is proposed for capturing users’ fresh interests. Specifically, the graph structure features on user-service interactions and the correlations between users and different sequences are extracted by the graph contrastive learning module. Then, social consistency sampling based on the graph convolutional network is adopted to filter out noise information effectively. Finally, time-sliced representations on the dual side (user, service) are integrated to capture users’ evolving interests by employing …