Summary of A Privacy-preserving Framework with Multi-modal Data For Cross-domain Recommendation, by Li Wang et al.
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation
by Li Wang, Lei Sang, Quangui Zhang, Qiang Wu, Min Xu
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework for cross-domain recommendation (CDR) that addresses the challenges of extracting domain-common and domain-specific features. The authors propose a Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation, called P2M2-CDR. This framework utilizes multi-modal information to disentangle more informative domain-common and domain-specific embeddings, while also incorporating local differential privacy (LDP) to obfuscate the disentangled embeddings before inter-domain exchange. The authors demonstrate that their approach outperforms other state-of-the-art single-domain and cross-domain baselines on four real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better recommendations when we don’t have a lot of data in one area, but there’s more information in another area. It uses special techniques to take the good things from both areas and combine them into something useful. This is important because it can help keep people’s personal information safe while still making good recommendations. |
Keywords
» Artificial intelligence » Multi modal
