A Privacy-Preserving Method for Sequential Recommendation in Vertical Federated Learning

Sequential recommendation is a challenging task that aims to predict the next item that a user will interact with based on their historical behavior sequence. Existing methods for sequential recommendation usually require a large amount of user behavior data to be collected and stored on a central server, which may raise privacy and security issues. In this paper, we propose a novel vertical federated framework for sequential recommendation, named FedSeqRec, where multiple clients share a common set of users but have non-overlapping items. FedSeqRec leverages user attributes and behavior sequences to generate representations of users, and adopts local differential privacy techniques to protect user data during model training. We conduct extensive experiments on three real-world datasets and demonstrate that FedSeqRec achieves competitive or even superior recommendation performance compared with several state-of-the-art methods, while enhancing privacy protection for user data.