Personalized Consumer Federated Recommender System Using Fine-Grained Transformation and Hybrid Information Sharing

Electronic shopping’s convenience and efficiency make it essential in modern life. In trustworthy personalized consumer recommender scenarios, diverse consumer interests lead to various interactions. Existing methods struggle to capture complex behavior dependencies and shared information across behaviors by exploring multi-behavior interaction sequences. To address this, we propose the Personalized Consumer Federated Recommender System Using Fine-grained Transformation and Hybrid Information Sharing (PCFedRec). Our contributions are as follows: Firstly, we employ the Fine-grained Transformation Module to capture fine-grained heterogeneous dependencies of consumer behaviors and model the behavior semantics of…

• NN
  National Natural Science Foundation of China

  Awards: 61972182, No.61972182, 62472200, No.62472200
• NK
  National Key Laboratory Foundation of China