MuKGB-CRS: Guarantee privacy and authenticity of cross-domain recommendation via multi-feature knowledge graph integrated blockchain

Li-e Wang, Yuelan Qi, Yan Bai, Zhigang Sun, Dongcheng Li, Xianxian Li

Abstract: In distributed cross-domain recommendation systems, current privacy protection techniques are ineffective at verifying data correctness and existing authenticity techniques have limitations in privacy protection. This poses a conflict between security and credibility. This paper designs a trusted cross-domain recommendation model based on multi-feature knowledge graph (MuKG) and blockchain to boost recommendation accuracy, data credibility and security. To the best of our knowledge, MuKG is proposed, for the first time, to realize a unified representation of multi-source heterogeneous data in a secure manner. Under this model, federated learning integrated with blockchain is used to implement a co-trust mechanism for distributed learning while guaranteeing the credibility of data through blockchain traceability. Our model can guarantee both data security and authenticity with no need of generalization for privacy protection and coordination of centralized servers for distributed controls. Experiments are performed on the two classic datasets, MovieLens and Amazon, with different sparsity. The results have shown that MuKG improves the recommendation accuracy by 1.5 % and diversity by 18 % while ensuring data security and credibility in sparse data. We also verify that different data characteristics have different influence on the recommendation results.