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.

翻译：跨域序列推荐（CSR）利用来自多个域的用户序列数据，近年来受到广泛关注。然而，现有CSR方法需要跨域共享原始用户数据，这违反了《通用数据保护条例》（GDPR）。因此，有必要将联邦学习（FL）与CSR相结合，在保护数据隐私的同时充分利用不同领域的知识。然而，跨域序列特征的异质性显著影响了FL的整体性能。本文提出FedDCSR，一种通过解耦表征学习实现的新型联邦跨域序列推荐框架。具体而言，为应对跨域序列特征的异质性，我们引入了一种称为域内-域间序列表示解耦（SRD）的方法，将用户序列特征解耦为域共享特征和域专属特征。此外，我们设计了一种域内对比信息最大化（CIM）策略，通过对用户序列进行数据增强来学习更丰富的用户域专属特征。在三个真实场景上的大量实验表明，FedDCSR相较于现有基线方法取得了显著性能提升。