仅用模型参数实现无重叠用户跨域推荐，保护隐私同时提升精度。
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation
- 基于联邦学习框架，仅使用其他域的模型参数进行知识迁移。
- 在无重叠用户场景下，相比基线提升12.3%的推荐准确率。
- 适合隐私敏感场景，如跨平台推荐系统设计。
现实中用户在不同服务间切换，具有多重兴趣，如观看热门短视频或直播。为更精准刻画用户兴趣以提升体验，近期研究提出跨域推荐技术，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精度。然而，传统方法通常依赖跨域存在重叠用户，并共享整体信息，包括用户历史日志、用户/物品嵌入及模型参数。但在实际推荐系统设计中，这些用户侧数据往往不可得，因用户可能完全不重叠，或受隐私政策限制无法共享个性化信息。因此，一个关键且有价值的问题浮现：如何仅利用其他域的模型参数检查点，提升目标域的预测准确率？为此，本文提出FMoE-CDSR，从联邦学习视角探索无重叠跨域序列推荐场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。