FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning

TL;DR

FedDCSR is a federated learning framework for cross-domain sequential recommendation that uses disentangled representation learning to handle feature heterogeneity and preserve user privacy.

Contribution

It introduces a novel disentangled representation learning approach and contrastive strategy to improve federated cross-domain sequential recommendation performance.

Findings

Significant improvements over existing baselines in real-world scenarios.

Effective disentanglement of shared and exclusive user features.

Enhanced learning of domain-specific features through data augmentation.

Abstract

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.