A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation

• URL: http://arxiv.org/abs/2403.03600v1
• Date: Wed, 6 Mar 2024 10:40:08 GMT
• Title: A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation
• Authors: Li Wang, Lei Sang, Quangui Zhang, Qiang Wu, Min Xu
• Abstract summary: Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data. We propose a Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation, called P2M2-CDR.
• Score: 13.33679167416221
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing the data-sparsity problem. Some existing CDR methods highlight the advantages of extracting domain-common and domain-specific features to learn comprehensive user and item representations. However, these methods can't effectively disentangle these components as they often rely on simple user-item historical interaction information (such as ratings, clicks, and browsing), neglecting the rich multi-modal features. Additionally, they don't protect user-sensitive data from potential leakage during knowledge transfer between domains. To address these challenges, we propose a Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation, called P2M2-CDR. Specifically, we first design a multi-modal disentangled encoder that utilizes multi-modal information to disentangle more informative domain-common and domain-specific embeddings. Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer. Local differential privacy (LDP) is utilized to obfuscate the disentangled embeddings before inter-domain exchange, thereby enhancing privacy protection. To ensure both consistency and differentiation among these obfuscated disentangled embeddings, we incorporate contrastive learning-based domain-inter and domain-intra losses. Extensive Experiments conducted on four real-world datasets demonstrate that P2M2-CDR outperforms other state-of-the-art single-domain and cross-domain baselines.

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