Cross-domain sequential recommendation is an important development direction of recommender systems. It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of users and alleviate the problem of cold-start users. However, in recent years, people pay more and more attention to their privacy. They do not want other people to know what they just bought, what videos they just watched, and where they just came from. How to protect the users' privacy has become an urgent problem to be solved. In this paper, we propose a novel privacy-preserving cross-domain sequential recommender system (PriCDSR), which can provide users with recommendation services while preserving their privacy at the same time. Specifically, we define a new differential privacy on the data, taking into account both the ID information and the order information. Then, we design a random mechanism that satisfies this differential privacy and provide its theoretical proof. Our PriCDSR is a non-invasive method that can adopt any cross-domain sequential recommender system as a base model without any modification to it. To the best of our knowledge, our PriCDSR is the first work to investigate privacy issues in cross-domain sequential recommender systems. We conduct experiments on three domains, and the results demonstrate that our PriCDSR, despite introducing noise, still outperforms recommender systems that only use data from a single domain.

翻译：跨域序列推荐是推荐系统的一个重要发展方向，它结合了序列推荐系统和跨域推荐系统的特点，能够捕捉用户的动态偏好，并缓解冷启动用户问题。然而，近年来人们越来越重视自身隐私，不希望他人了解自己的购物记录、视频观看历史或行程轨迹。如何保护用户隐私已成为亟待解决的问题。本文提出了一种新型隐私保护跨域序列推荐系统（PriCDSR），该系统能在提供推荐服务的同时保护用户隐私。具体而言，我们针对数据定义了新的差分隐私，同时考虑了身份信息与顺序信息；随后设计了满足该差分隐私的随机机制，并提供了理论证明。PriCDSR是一种非侵入式方法，可直接采用任意跨域序列推荐系统作为基座模型而无需修改。据我们所知，PriCDSR是首个研究跨域序列推荐系统中隐私问题的成果。我们在三个域上进行了实验，结果表明，尽管引入噪声，PriCDSR的性能仍优于仅使用单域数据的推荐系统。