解决用户行为序列推荐中的隐私问题，实现本地训练与全局模型协同。
A Systematic Survey on Federated Sequential Recommendation
- 采用联邦学习框架，用户数据本地训练不上传
- 平衡隐私保护与推荐准确率，提升系统安全性
- 适合注重数据隐私的推荐系统研究者与开发者
序列推荐是一种利用用户行为序列建模时间依赖和偏好模式的先进推荐技术。然而，传统方法需集中收集用户数据，威胁数据隐私。近年来，联邦学习作为一种分布式架构，使参与者在本地保留私有数据的同时协同训练全局模型。本综述首次提出联邦序列推荐（FedSR），让每位用户作为参与方加入联邦训练，实现兼顾数据隐私与模型性能的推荐服务。文章首先介绍FedSR的背景与独特挑战，随后从两个层面回顾现有解决方案，每层包含两种具体技术。此外，还探讨了当前关键挑战与未来研究方向。
原文摘要 · Abstract (English)
Sequential recommendation is an advanced recommendation technique that utilizes the sequence of user behaviors to generate personalized suggestions by modeling the temporal dependencies and patterns in user preferences. However, it requires a server to centrally collect users' data, which poses a threat to the data privacy of different users. In recent years, federated learning has emerged as a distributed architecture that allows participants to train a global model while keeping their private data locally. This survey pioneers Federated Sequential Recommendation (FedSR), where each user joins as a participant in federated training to achieve a recommendation service that balances data privacy and model performance. We begin with an introduction to the background and unique challenges of FedSR. Then, we review existing solutions from two levels, each of which includes two specific techniques. Additionally, we discuss the critical challenges and future research directions in FedSR.
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