In the real world, users always have multiple interests while surfing different services to enrich their daily lives, e.g., watching hot short videos/live streamings. To describe user interests precisely for a better user experience, the recent literature proposes cross-domain techniques by transferring the other related services (a.k.a. domain) knowledge to enhance the accuracy of target service prediction. In practice, naive cross-domain techniques typically require there exist some overlapped users, and sharing overall information across domains, including user historical logs, user/item embeddings, and model parameter checkpoints. Nevertheless, other domain's user-side historical logs and embeddings are not always available in real-world RecSys designing, since users may be totally non-overlapped across domains, or the privacy-preserving policy limits the personalized information sharing across domains. Thereby, a challenging but valuable problem is raised: How to empower target domain prediction accuracy by utilizing the other domain model parameters checkpoints only? To answer the question, we propose the FMoE-CDSR, which explores the non-overlapped cross-domain sequential recommendation scenario from the federated learning perspective.

翻译：在现实世界中，用户为丰富日常生活（例如观看热门短视频/直播）而使用不同服务时，往往具有多重兴趣。为精确描述用户兴趣以提升用户体验，近期研究提出跨域技术，通过迁移其他相关服务（即领域）的知识来增强目标服务的预测准确性。实践中，传统的跨域技术通常要求存在部分重叠用户，并需跨域共享整体信息，包括用户历史日志、用户/物品嵌入向量及模型参数检查点。然而，在实际推荐系统设计中，其他领域的用户端历史日志与嵌入向量往往难以获取，因为用户在不同领域间可能完全无重叠，或隐私保护政策限制了跨域的个性化信息共享。由此引出一个具有挑战性但极具价值的问题：如何仅利用其他领域的模型参数检查点来提升目标领域的预测精度？为回答该问题，我们提出FMoE-CDSR模型，从联邦学习的视角探索非重叠跨域序列推荐场景。