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.

翻译：跨领域推荐旨在通过利用源领域的丰富信息来提升目标领域在稀疏数据下的推荐准确性，从而缓解数据稀疏问题。现有一些跨领域推荐方法通过提取领域公共特征和领域特有特征来学习全面的用户与物品表示，并展现了显著优势。然而，这些方法难以有效解耦上述成分，因为它们通常仅依赖简单的用户-物品历史交互信息（如评分、点击和浏览记录），忽视了丰富的多模态特征。此外，这些方法在跨领域知识迁移过程中无法保护用户敏感数据免受潜在泄露。为解决上述挑战，我们提出了一种面向跨领域推荐的多模态数据隐私保护框架P2M2-CDR。具体而言，我们首先设计了一种多模态解耦编码器，利用多模态信息解耦出更具信息量的领域公共嵌入和领域特有嵌入。其次，我们引入隐私保护解码器以减少知识迁移过程中的用户隐私泄露。在跨领域交换前，采用本地差分隐私技术对解耦后的嵌入进行混淆处理，从而增强隐私保护。为确保这些经过混淆的解耦嵌入既保持一致性又体现差异性，我们引入了基于对比学习的领域间损失和领域内损失。在四个真实世界数据集上进行的大量实验表明，P2M2-CDR的性能优于其他最先进的单领域和跨领域基线方法。