{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:39:27Z","timestamp":1775932767118,"version":"3.50.1"},"reference-count":184,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>\n            Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. However, success has been accompanied with a major new arising challenge:\n            <jats:italic>Many applications of machine learning (ML) are adversarial in nature<\/jats:italic>\n            [146]. In recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs.\n          <\/jats:p>\n          <jats:p>The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models) and (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 76 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community working on the security of RS or on generative models using GANs to improve their quality.<\/jats:p>","DOI":"10.1145\/3439729","type":"journal-article","created":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T04:09:57Z","timestamp":1615003797000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":182,"title":["A Survey on Adversarial Recommender Systems"],"prefix":"10.1145","volume":"54","author":[{"given":"Yashar","family":"Deldjoo","sequence":"first","affiliation":[{"name":"Polytechnic University of Bari, Italy"}]},{"given":"Tommaso Di","family":"Noia","sequence":"additional","affiliation":[{"name":"Polytechnic University of Bari, Italy"}]},{"given":"Felice Antonio","family":"Merra","sequence":"additional","affiliation":[{"name":"Polytechnic University of Bari, Italy"}]}],"member":"320","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-019-09256-1"},{"key":"e_1_2_1_2_1","volume-title":"Recommender Systems","author":"Aggarwal Charu C.","unstructured":"Charu C. Aggarwal . 2016. Ensemble-based and hybrid recommender systems . In Recommender Systems . Springer , 199--224. Charu C. Aggarwal. 2016. Ensemble-based and hybrid recommender systems. In Recommender Systems. Springer, 199--224."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"e_1_2_1_5_1","volume-title":"Tommaso Di Noia, and Felice Antonio Merra","author":"Anelli Vito Walter","year":"2020","unstructured":"Vito Walter Anelli , Alejandro Bellog\u00edn , Yashar Deldjoo , Tommaso Di Noia, and Felice Antonio Merra . 2020 . Multi-step adversarial perturbations on recommender systems embeddings. arXiv 2010.01329. Vito Walter Anelli, Alejandro Bellog\u00edn, Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. Multi-step adversarial perturbations on recommender systems embeddings. arXiv 2010.01329."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-35166-3_34"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3412841.3442010"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3411447"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the Doctoral Consortium co-located with the Conference of the Italian Association for Artificial Intelligence (DDC@AI*IA\u201920)","author":"Anelli Vito Walter","year":"2020","unstructured":"Vito Walter Anelli , Tommaso Di Noia , Daniele Malitesta , and Felice Antonio Merra . 2020 . Assessing perceptual and recommendation mutation of adversarially poisoned visual recommenders . In Proceedings of the Doctoral Consortium co-located with the Conference of the Italian Association for Artificial Intelligence (DDC@AI*IA\u201920) . CEUR-WS.org. Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, and Felice Antonio Merra. 2020. Assessing perceptual and recommendation mutation of adversarially poisoned visual recommenders. In Proceedings of the Doctoral Consortium co-located with the Conference of the Italian Association for Artificial Intelligence (DDC@AI*IA\u201920). CEUR-WS.org."},{"key":"e_1_2_1_10_1","volume-title":"Daniele Malitesta, and Felice Antonio Merra.","author":"Anelli Vito Walter","year":"2020","unstructured":"Vito Walter Anelli , Tommaso Di Noia , Daniele Malitesta, and Felice Antonio Merra. 2020 . An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders . arxiv:2010.00984 Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, and Felice Antonio Merra. 2020. An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders. arxiv:2010.00984"},{"key":"e_1_2_1_11_1","volume-title":"CoRR abs\/1701.07875","author":"Arjovsky Mart\u00edn","year":"2017","unstructured":"Mart\u00edn Arjovsky , Soumith Chintala , and L\u00e9on Bottou . 2017. Wasserstein GAN. CoRR abs\/1701.07875 ( 2017 ). Mart\u00edn Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein GAN. CoRR abs\/1701.07875 (2017)."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371832"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043932.2043996"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2789995"},{"key":"e_1_2_1_15_1","volume-title":"BEGAN: Boundary equilibrium generative adversarial networks. CoRR abs\/1703.10717","author":"Berthelot David","year":"2017","unstructured":"David Berthelot , Tom Schumm , and Luke Metz . 2017 . 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