Privacy-preserving cross-domain recommendation using hybrid federated transfer learning

Cross-domain recommender systems, which serve as a panacea to data sparsity and cold-start problems, are increasingly vital for providing personalized content across diverse application domains like medicine, e-commerce, etc. This is accomplished by leveraging adequate ratings ad user profiles in one domain to ensure precise recommendations in another domain. However, the domains with good ratings are unwilling to share user ratings with third-party recommender systems/domains due to privacy-concern. It demands the need for privacy mechanism to transfer knowledge between domains securely. So, this research introduces the CrossRec-Hybrid Federated Transfer Learning (CrossRec-HFTL) framework to address cold start and privacy problems in a cross-domain recommendation system, which combines adversarial domain adaptation and federated learning to leverage user and item interactions from a labeled source domain to generate recommendations in an unlabeled target domain. CrossRec-HFTL uses a model architecture called CrossRec-CDAN, which consists of multiple generators and discriminators for domain adaptation. The generators produce user, item, and interaction embeddings, while the discriminators distinguish between source and target domain samples. Federated learning enables collaborative model training across multiple clients while maintaining data locality, further bolstered by encryption techniques for privacy preservation. The framework was evaluated on the Amazon dataset across three domain pairs: Home and kitchen to Office Products (HK → OP), CDs and vinyl to Digital Music (CDs → DM) and Digital Music to Music Instruments (DM → MI). The simulation findings reveal that CrossRec-HFTL outperforms baseline and modern recommendation models in terms of recommendation accuracy, achieving a very less RMSE of 0.82 and MAE of 0.58. This shows the robust performance of CrossRec-HFTL in cold-start scenarios and enhanced privacy preservation.