FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning
This addresses privacy concerns in cross-domain recommendation for users and platforms, though it is incremental as it builds on existing federated learning and CSR methods.
The paper tackles the problem of cross-domain sequential recommendation while preserving user data privacy by combining federated learning with disentangled representation learning, resulting in FedDCSR, which shows significant improvements over baselines in experiments on three real-world scenarios.
Cross-domain Sequential Recommendation (CSR) which leverages user sequence data from multiple domains has received extensive attention in recent years. However, the existing CSR methods require sharing origin user data across domains, which violates the General Data Protection Regulation (GDPR). Thus, it is necessary to combine federated learning (FL) and CSR to fully utilize knowledge from different domains while preserving data privacy. Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL. In this paper, we propose FedDCSR, a novel federated cross-domain sequential recommendation framework via disentangled representation learning. Specifically, to address the sequence feature heterogeneity across domains, we introduce an approach called inter-intra domain sequence representation disentanglement (SRD) to disentangle the user sequence features into domain-shared and domain-exclusive features. In addition, we design an intra domain contrastive infomax (CIM) strategy to learn richer domain-exclusive features of users by performing data augmentation on user sequences. Extensive experiments on three real-world scenarios demonstrate that FedDCSR achieves significant improvements over existing baselines.