Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation

TL;DR

This paper introduces a federated learning framework for cross-domain recommendation that enhances privacy by learning comprehensive user preferences from multiple data sources and securely transferring prototypes, outperforming existing methods.

Contribution

It proposes a novel federated user preference modeling framework that incorporates side information and privacy-preserving prototype transfer, addressing limitations of prior privacy leakage and preference accuracy.

Findings

FUPM outperforms state-of-the-art baselines on four CDR tasks.

The comprehensive preference exploration improves user preference accuracy.

Federated prototype transfer enhances privacy protection.

Abstract

Cross-domain recommendation (CDR) aims to address the data-sparsity problem by transferring knowledge across domains. Existing CDR methods generally assume that the user-item interaction data is shareable between domains, which leads to privacy leakage. Recently, some privacy-preserving CDR (PPCDR) models have been proposed to solve this problem. However, they primarily transfer simple representations learned only from user-item interaction histories, overlooking other useful side information, leading to inaccurate user preferences. Additionally, they transfer differentially private user-item interaction matrices or embeddings across domains to protect privacy. However, these methods offer limited privacy protection, as attackers may exploit external information to infer the original data. To address these challenges, we propose a novel Federated User Preference Modeling (FUPM) framework. In FUPM, first, a novel comprehensive preference exploration module is proposed to learn users' comprehensive preferences from both interaction data and additional data including review texts and potentially positive items. Next, a private preference transfer module is designed to first learn differentially private local and global prototypes, and then privately transfer the global prototypes using a federated learning strategy. These prototypes are generalized representations of user groups, making it difficult for attackers to infer individual information. Extensive experiments on four CDR tasks conducted on the Amazon and Douban datasets validate the superiority of FUPM over SOTA baselines. Code is available at https://github.com/Lili1013/FUPM.