Dual-Contrastive for Federated Social Recommendation
Linze Luo, Baisong Liu · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Existing federated social recommendation systems mainly focus on protecting users' data privacy, and the hetero-geneity of data distribution among users is less mentioned. A key reason for the poor performance of federated social recommendation systems compared to traditional ones is the heterogeneity of the local data in the federated setting. Also, it severely limits the development of federated social recommendation systems. This paper proposes a federated social recommendation framework based on Contrastive Learning. We use contrastive learning to minimize the distance between a user and his trusted users on the user level's feature space and maximize the consistency between local and global item embeddings for item embedding. Corrections for updates to user embeddings and item embeddings alleviate the performance impact of heterogeneous data distributions. Experiments on three datasets show that our approach significantly improves performance over existing methods.