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Expand Up @@ -45,6 +45,7 @@ The table is complement of the survey below.
### ADVERSARIAL MACHINE LEARNING FOR SECURITY OF RS
| Year | Title | Type | Target Model | Venue | Link | Code |
|-------|--------|--------|--------|-----------|---------------|---------|
|2020|**Revisiting Adversarially Learned Injection Attacks Against Recommender Systems**| Attack | MF,NCF,MultiVAE,ItemCF | RecSys | [Link](https://dl.acm.org/doi/10.1145/3383313.3412243)| [Code](https://github.com/graytowne/revisit_adv_rec)|
|2020|**Adversarial Learning for Recommendation: Applications for Security and Generative Tasks — Concept to Code**| Tutorial | BPR-MF | RecSys | [Link](https://dl.acm.org/doi/abs/10.1145/3383313.3411447)| [Hands-On](https://github.com/sisinflab/HandsOn-RecSys2020)|
|2020|**TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems**| Attack | VBPR/AMR | DSN-DSML | [Link](https://ieeexplore.ieee.org/document/9151572)| [Code](https://github.com/sisinflab/TAaMR)|
|2020|**Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start**| Attack | VBPR | arXiv| [Link](https://arxiv.org/abs/2006.01888)| |
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