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Assessing the Impact of a User-Item Collaborative Attack on Class of Users.

, , and . ImpactRS@RecSys, volume 2462 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)

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Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries., , , , and . SIGIR, page 1924-1928. ACM, (2023)A Regression Framework to Interpret the Robustness of Recommender Systems Against Shilling Attacks., , , and . IIR, volume 2947 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems., , and . DSN Workshops, page 1-8. IEEE, (2020)Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation., , , , , , , and . SIGIR, page 2405-2414. ACM, (2021)Knowledge-enhanced Shilling Attacks for Recommendation., , , , , and . SEBD, volume 2646 of CEUR Workshop Proceedings, page 310-317. CEUR-WS.org, (2020)V-Elliot: Design, Evaluate and Tune Visual Recommender Systems., , , , , , , and . RecSys, page 768-771. ACM, (2021)Assessing the Impact of a User-Item Collaborative Attack on Class of Users., , and . ImpactRS@RecSys, volume 2462 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models., , , and . SIGIR, page 951-960. ACM, (2020)MSAP: Multi-Step Adversarial Perturbations on Recommender Systems Embeddings., , , , and . FLAIRS, (2021)Assessing Perceptual and Recommendation Mutation of Adversarially-Poisoned Visual Recommenders (short paper)., , , and . DP@AI*IA, volume 2776 of CEUR Workshop Proceedings, page 49-56. CEUR-WS.org, (2020)