mlsanityData to 7 Oct 2026

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

arXiv 2403.03600 ↗First posted 6 Mar 2024cs.AIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

27 of 54 references.

References · 27

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  4. 2017Neural Collaborative Filteringcited by 1,108
  5. 2015Image-based Recommendations on Styles and Substitutescited by 702
  6. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  7. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  8. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
  9. 2020Disentangled Graph Collaborative Filteringcited by 118
  10. 2019Learning Disentangled Representations for Recommendationcited by 110
  11. 2019Secure Federated Matrix Factorizationcited by 102
  12. 2018CoNet: Collaborative Cross Networks for Cross-Domain Recommendationcited by 81
15 more
  1. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  2. 2021Cross-Domain Recommendation: Challenges, Progress, and Prospectscited by 74
  3. 2019DDTCDR: Deep Dual Transfer Cross Domain Recommendationcited by 71
  4. 2020CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Networkcited by 42
  5. 2021Personalized Transfer of User Preferences for Cross-domain Recommendationcited by 34
  6. 2021Federated Neural Collaborative Filteringcited by 30
  7. 2020A Deep Framework for Cross-Domain and Cross-System Recommendationscited by 24
  8. 2022Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendationcited by 22
  9. 2018Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviewscited by 19
  10. 2021A Unified Framework for Cross-Domain and Cross-System Recommendationscited by 19
  11. 2020Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptationcited by 10
  12. 2023PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendationcited by 9
  13. 2022FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendationcited by 6
  14. 2022Heterogeneous Graph Neural Network for Privacy-Preserving Recommendationcited by 6
  15. 2023Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2024Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learningcited by 1
  2. 2025FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendationscited by 0
  3. 2025Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendationcited by 0
  4. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  5. 2026MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendationcited by 0