Summary of Securing Recommender System Via Cooperative Training, by Qingyang Wang et al.
Securing Recommender System via Cooperative Training
by Qingyang Wang, Chenwang Wu, Defu Lian, Enhong Chen
First submitted to arxiv on: 23 Jan 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 The paper proposes a novel framework, Triple Cooperative Defense (TCD), to enhance recommendation robustness against well-crafted fake profiles. TCD integrates data processing and model-based approaches, leveraging three cooperative models that mutually improve data quality and recommendation accuracy. Additionally, the authors introduce two attack strategies, Co-training Attack (Co-Attack) and Game-based Co-training Attack (GCoAttack), which optimize attacks in a bi-level setting while maintaining efficiency. The proposed framework outperforms existing methods on three real-world datasets, demonstrating its effectiveness in enhancing model robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make recommendation systems safer by showing that fake profiles can be very bad for the system. They propose new ways to stop these fake profiles from being so effective and show that their ideas work better than what others have done before. The authors also come up with two new types of attacks that are more realistic because they take into account how the system is actually set up. |
