{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:03:12Z","timestamp":1777658592088,"version":"3.51.4"},"reference-count":195,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n            With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters \u201cdirty\u201d data, where noise or malicious information can lead to abnormal recommendations. Research on improving robustness of recommender systems against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on robust recommender systems. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training for defending against malicious attacks, and regularization, purification, self-supervised learning for defending against malicious attacks. Additionally, we summarize evaluation metrics and commonly used datasets for assessing robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to provide readers with a comprehensive understanding of robust recommender systems and to identify key pathways for future research and development. To facilitate ongoing exploration, we maintain a continuously updated GitHub repository with related research:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Kaike-Zhang\/Robust-Recommender-System\">https:\/\/github.com\/Kaike-Zhang\/Robust-Recommender-System<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3757057","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T11:04:43Z","timestamp":1753787083000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Robust Recommender System: A Survey and Future Directions"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1197-5212","authenticated-orcid":false,"given":"Kaike","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3454-4789","authenticated-orcid":false,"given":"Qi","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6146-148X","authenticated-orcid":false,"given":"Fei","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6994-6791","authenticated-orcid":false,"given":"Yunfan","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6113-6145","authenticated-orcid":false,"given":"Shuchang","family":"Tao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1081-8119","authenticated-orcid":false,"given":"Huawei","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5201-8195","authenticated-orcid":false,"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of AI Safety, Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,9]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"308","volume-title":"SIGSAC","author":"Abadi Martin","year":"2016","unstructured":"Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In SIGSAC. 308\u2013318."},{"issue":"20","key":"e_1_3_3_3_2","doi-asserted-by":"crossref","first-page":"9608","DOI":"10.3390\/app11209608","article-title":"Eliciting auxiliary information for cold start user recommendation: A survey","volume":"11","author":"Abdullah Nor Aniza","year":"2021","unstructured":"Nor Aniza Abdullah, Rasheed Abubakar Rasheed, Mohd Hairul Nizam Md Nasir, and Md Mujibur Rahman. 2021. Eliciting auxiliary information for cold start user recommendation: A survey. Appl. Sci. 11, 20 (2021), 9608.","journal-title":"Appl. Sci."},{"issue":"7","key":"e_1_3_3_4_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3543846","article-title":"Reinforcement learning based recommender systems: A survey","volume":"55","author":"Afsar M. Mehdi","year":"2022","unstructured":"M. Mehdi Afsar, Trafford Crump, and Behrouz Far. 2022. Reinforcement learning based recommender systems: A survey. ACM Comput. Surv. 55, 7 (2022), 1\u201338.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_5_2","first-page":"348","volume-title":"RecSys","author":"Aktukmak Mehmet","year":"2019","unstructured":"Mehmet Aktukmak, Yasin Yilmaz, and Ismail Uysal. 2019. Quick and accurate attack detection in recommender systems through user attributes. In RecSys. 348\u2013352."},{"issue":"5","key":"e_1_3_3_6_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3527449","article-title":"Survey on the objectives of recommender systems: Measures, solutions, evaluation methodology, and new perspectives","volume":"55","author":"Alhijawi Bushra","year":"2022","unstructured":"Bushra Alhijawi, Arafat Awajan, and Salam Fraihat. 2022. Survey on the objectives of recommender systems: Measures, solutions, evaluation methodology, and new perspectives. ACM Comput. Surv. 55, 5 (2022), 1\u201338.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_7_2","first-page":"1094","volume-title":"SIGIR","author":"Anelli Vito Walter","year":"2021","unstructured":"Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, and Felice Antonio Merra. 2021. A study of defensive methods to protect visual recommendation against adversarial manipulation of images. In SIGIR. 1094\u20131103."},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1145\/3383313.3411447","volume-title":"RecSys","author":"Anelli Vito Walter","year":"2020","unstructured":"Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2020. Adversarial learning for recommendation: Applications for security and generative tasks\u2013concept to code. In RecSys. 738\u2013741."},{"key":"e_1_3_3_9_2","first-page":"335","volume-title":"Recommender Systems Handbook","author":"Anelli Vito Walter","year":"2021","unstructured":"Vito Walter Anelli, Yashar Deldjoo, Tommaso DiNoia, and Felice Antonio Merra. 2021. Adversarial recommender systems: Attack, defense, and advances. In Recommender Systems Handbook. Springer, 335\u2013379."},{"key":"e_1_3_3_10_2","first-page":"301","volume-title":"RecSys","author":"Baltrunas Linas","year":"2011","unstructured":"Linas Baltrunas, Bernd Ludwig, and Francesco Ricci. 2011. Matrix factorization techniques for context aware recommendation. In RecSys. 301\u2013304."},{"key":"e_1_3_3_11_2","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1145\/3531146.3533090","volume-title":"FACCT","author":"Bell Andrew","year":"2022","unstructured":"Andrew Bell, Ian Solano-Kamaiko, Oded Nov, and Julia Stoyanovich. 2022. It\u2019s just not that simple: An empirical study of the accuracy-explainability trade-off in machine learning for public policy. In FACCT. 248\u2013266."},{"key":"e_1_3_3_12_2","first-page":"97","volume-title":"TheWebConf","author":"Beutel Alex","year":"2014","unstructured":"Alex Beutel, Kenton Murray, Christos Faloutsos, and Alexander J. Smola. 2014. Cobafi: Collaborative Bayesian filtering. In TheWebConf. 97\u2013108."},{"key":"e_1_3_3_13_2","first-page":"400","volume-title":"RecSys","author":"Bian Zhi","year":"2021","unstructured":"Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, Kaikui Liu, and Xiaolong Li. 2021. Denoising user-aware memory network for recommendation. In RecSys. 400\u2013410."},{"key":"e_1_3_3_14_2","first-page":"93","volume-title":"SIGMOD","author":"Breunig Markus M.","year":"2000","unstructured":"Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and J\u00f6rg Sander. 2000. LOF: Identifying density-based local outliers. In SIGMOD. 93\u2013104."},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1023\/A:1021240730564","article-title":"Hybrid recommender systems: Survey and experiments","volume":"12","author":"Burke Robin","year":"2002","unstructured":"Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact. 12, 4 (2002), 331\u2013370.","journal-title":"User Model. User-Adapt. Interact."},{"key":"e_1_3_3_16_2","first-page":"542","volume-title":"SIGKDD","author":"Burke Robin","year":"2006","unstructured":"Robin Burke, Bamshad Mobasher, Chad Williams, and Runa Bhaumik. 2006. Classification features for attack detection in collaborative recommender systems. In SIGKDD. 542\u2013547."},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-7637-6_28","article-title":"Robust collaborative recommendation","author":"Burke Robin","year":"2015","unstructured":"Robin Burke, Michael P. O\u2019Mahony, and Neil J. Hurley. 2015. Robust collaborative recommendation. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). Springer, 961\u2013995.","journal-title":"Recommender Systems Handbook"},{"key":"e_1_3_3_18_2","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s11280-012-0164-6","article-title":"Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system","volume":"16","author":"Cao Jie","year":"2013","unstructured":"Jie Cao, Zhiang Wu, Bo Mao, and Yanchun Zhang. 2013. Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web 16, 5 (2013), 729\u2013748.","journal-title":"World Wide Web"},{"key":"e_1_3_3_19_2","first-page":"1669","volume-title":"SIGIR","author":"Cao Yuanjiang","year":"2020","unstructured":"Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, and Wei Emma Zhang. 2020. Adversarial attacks and detection on reinforcement learning-based interactive recommender systems. In SIGIR. 1669\u20131672."},{"key":"e_1_3_3_20_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-13287-2","volume-title":"Music Recommendation and Discovery in the Long Tail","author":"Celma O.","year":"2010","unstructured":"O. Celma. 2010. Music Recommendation and Discovery in the Long Tail. Springer."},{"key":"e_1_3_3_21_2","first-page":"363","volume-title":"RecSys","author":"Chen Huiyuan","year":"2019","unstructured":"Huiyuan Chen and Jing Li. 2019. Adversarial tensor factorization for context-aware recommendation. In RecSys. 363\u2013367."},{"key":"e_1_3_3_22_2","first-page":"245","volume-title":"RecSys","author":"Chen Huiyuan","year":"2023","unstructured":"Huiyuan Chen, Xiaoting Li, Vivian Lai, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Mahashweta Das, and Hao Yang. 2023. Adversarial collaborative filtering for free. In RecSys. 245\u2013255."},{"key":"e_1_3_3_23_2","first-page":"92","volume-title":"RecSys","author":"Chen Huiyuan","year":"2022","unstructured":"Huiyuan Chen, Yusan Lin, Menghai Pan, Lan Wang, Chin-Chia Michael Yeh, Xiaoting Li, Yan Zheng, Fei Wang, and Hao Yang. 2022. Denoising self-attentive sequential recommendation. In RecSys. 92\u2013101."},{"key":"e_1_3_3_24_2","first-page":"1854","volume-title":"SIGIR","author":"Chen Huiyuan","year":"2022","unstructured":"Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Xia Hu, Fei Wang, and Hao Yang. 2022. Adversarial graph perturbations for recommendations at scale. In SIGIR. 1854\u20131858."},{"issue":"3","key":"e_1_3_3_25_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3564284","article-title":"Bias and debias in recommender system: A survey and future directions","volume":"41","author":"Chen Jiawei","year":"2023","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Trans. Inf. Syst. 41, 3 (2023), 1\u201339.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_3_26_2","first-page":"2172","volume-title":"TheWebConf","author":"Chen Yongjun","year":"2022","unstructured":"Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022. Intent contrastive learning for sequential recommendation. In TheWebConf. 2172\u20132182."},{"key":"e_1_3_3_27_2","doi-asserted-by":"crossref","unstructured":"Zheng Chen Ziyan Jiang Fan Yang Eunah Cho Xing Fan Xiaojiang Huang Yanbin Lu and Aram Galstyan. 2023. Graph meets LLM: A novel approach to collaborative filtering for robust conversational understanding. In EMNLP: Industry Track. 811\u2013819.","DOI":"10.18653\/v1\/2023.emnlp-industry.75"},{"key":"e_1_3_3_28_2","first-page":"7","volume-title":"RecSys","author":"Cheng Heng-Tze","year":"2016","unstructured":"Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et\u00a0al. 2016. Wide & deep learning for recommender systems. In RecSys. 7\u201310."},{"key":"e_1_3_3_29_2","first-page":"67","volume-title":"WIDM","author":"Chirita Paul-Alexandru","year":"2005","unstructured":"Paul-Alexandru Chirita, Wolfgang Nejdl, and Cristian Zamfir. 2005. Preventing shilling attacks in online recommender systems. In WIDM. 67\u201374."},{"key":"e_1_3_3_30_2","first-page":"322","volume-title":"RecSys","author":"Christakopoulou Konstantina","year":"2019","unstructured":"Konstantina Christakopoulou and Arindam Banerjee. 2019. Adversarial attacks on an oblivious recommender. In RecSys. 322\u2013330."},{"issue":"1","key":"e_1_3_3_31_2","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.dss.2013.01.020","article-title":"\\(\\beta\\) P: A novel approach to filter out malicious rating profiles from recommender systems","volume":"55","author":"Chung Chen-Yao","year":"2013","unstructured":"Chen-Yao Chung, Ping-Yu Hsu, and Shih-Hsiang Huang. 2013. \\(\\beta\\) P: A novel approach to filter out malicious rating profiles from recommender systems. Decis. Support Syst. 55, 1 (2013), 314\u2013325.","journal-title":"Decis. Support Syst."},{"key":"e_1_3_3_32_2","first-page":"1310","volume-title":"ICML","author":"Cohen Jeremy","year":"2019","unstructured":"Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019. Certified adversarial robustness via randomized smoothing. In ICML. PMLR, 1310\u20131320."},{"key":"e_1_3_3_33_2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1145\/3437963.3441757","volume-title":"WSDM","author":"Cohen Rami","year":"2021","unstructured":"Rami Cohen, Oren Sar Shalom, Dietmar Jannach, and Amihood Amir. 2021. A black-box attack model for visually-aware recommender systems. In WSDM. 94\u2013102."},{"key":"e_1_3_3_34_2","first-page":"46","volume-title":"AAAI","author":"Cooper A. Feder","year":"2021","unstructured":"A. Feder Cooper, Ellen Abrams, and Na Na. 2021. Emergent unfairness in algorithmic fairness-accuracy trade-off research. In AAAI. 46\u201354."},{"key":"e_1_3_3_35_2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1145\/2959100.2959190","volume-title":"RecSys","author":"Covington Paul","year":"2016","unstructured":"Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for YouTube recommendations. In RecSys. 191\u2013198."},{"issue":"10","key":"e_1_3_3_36_2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1145\/3409116","article-title":"A decade of social bot detection","volume":"63","author":"Cresci Stefano","year":"2020","unstructured":"Stefano Cresci. 2020. A decade of social bot detection. Commun. ACM 63, 10 (2020), 72\u201383.","journal-title":"Commun. ACM"},{"key":"e_1_3_3_37_2","unstructured":"Emiliano De Cristofaro. 2020. An overview of privacy in machine learning. arXiv preprint arXiv:2005.08679 (2020)."},{"key":"e_1_3_3_38_2","first-page":"951","volume-title":"SIGIR","author":"Deldjoo Yashar","year":"2020","unstructured":"Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, and Felice Antonio Merra. 2020. How dataset characteristics affect the robustness of collaborative recommendation models. In SIGIR. 951\u2013960."},{"issue":"2","key":"e_1_3_3_39_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439729","article-title":"A survey on adversarial recommender systems: From attack\/defense strategies to generative adversarial networks","volume":"54","author":"Deldjoo Yashar","year":"2021","unstructured":"Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2021. A survey on adversarial recommender systems: From attack\/defense strategies to generative adversarial networks. ACM Comput. Surv. 54, 2 (2021), 1\u201338.","journal-title":"ACM Comput. Surv."},{"issue":"3","key":"e_1_3_3_40_2","first-page":"555","article-title":"Enhancing the robustness of neural collaborative filtering systems under malicious attacks","volume":"21","author":"Du Yali","year":"2018","unstructured":"Yali Du, Meng Fang, Jinfeng Yi, Chang Xu, Jun Cheng, and Dacheng Tao. 2018. Enhancing the robustness of neural collaborative filtering systems under malicious attacks. IEEE Trans. Multimedia 21, 3 (2018), 555\u2013565.","journal-title":"IEEE Trans. Multimedia"},{"key":"e_1_3_3_41_2","first-page":"1","volume-title":"ICALP","author":"Dwork Cynthia","year":"2006","unstructured":"Cynthia Dwork. 2006. Differential privacy. In ICALP. Springer, 1\u201312."},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536466"},{"key":"e_1_3_3_43_2","doi-asserted-by":"crossref","unstructured":"Zihuai Zhao Wenqi Fan Jiatong Li Yunqing Liu Xiaowei Mei Yiqi Wang Zhen Wen Fei Wang Xiangyu Zhao Jiliang Tang and Qing Li. 2024. Recommender systems in the era of large language models (LLMs). IEEE TKDE 36 11 (2024) 6889\u20136907.","DOI":"10.1109\/TKDE.2024.3392335"},{"issue":"6","key":"e_1_3_3_44_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2017.2740965","article-title":"The robustness of deep networks: A geometrical perspective","volume":"34","author":"Fawzi Alhussein","year":"2017","unstructured":"Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard. 2017. The robustness of deep networks: A geometrical perspective. IEEE Signal Process. Mag. 34, 6 (2017), 50\u201362.","journal-title":"IEEE Signal Process. Mag."},{"key":"e_1_3_3_45_2","first-page":"1412","volume-title":"SIGIR","author":"Gao Yunjun","year":"2022","unstructured":"Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, and Baihua Zheng. 2022. Self-guided learning to denoise for robust recommendation. In SIGIR. 1412\u20131422."},{"key":"e_1_3_3_46_2","unstructured":"Yunfan Gao Tao Sheng Youlin Xiang Yun Xiong HaofenWang and Jiawei Zhang. 2023. Chat-REC: Towards interactive and explainable LLMs-augmented recommender system. arXiv preprint arXiv:2303.14524 (2023)."},{"key":"e_1_3_3_47_2","doi-asserted-by":"crossref","unstructured":"Yingqiang Ge Shuchang Liu Zuohui Fu Juntao Tan Zelong Li Shuyuan Xu Yunqi Li Yikun Xian and Yongfeng Zhang. 2024. A survey on trustworthy recommender systems. ACM Trans. Recomm. Syst. 3 2 (2024).","DOI":"10.1145\/3652891"},{"issue":"4","key":"e_1_3_3_48_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2843948","article-title":"The Netflix recommender system: Algorithms, business value, and innovation","volume":"6","author":"Gomez-Uribe Carlos A.","year":"2015","unstructured":"Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. 6, 4 (2015), 1\u201319.","journal-title":"ACM Trans. Manag. Inf. Syst."},{"key":"e_1_3_3_49_2","unstructured":"Ian J. Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In ICLR."},{"key":"e_1_3_3_50_2","first-page":"133","volume-title":"ICCCA","author":"Gope Jyotirmoy","year":"2017","unstructured":"Jyotirmoy Gope and Sanjay Kumar Jain. 2017. A survey on solving cold start problem in recommender systems. In ICCCA. IEEE, 133\u2013138."},{"key":"e_1_3_3_51_2","first-page":"547","volume-title":"Recommender Systems Handbook","author":"Gunawardana Asela","year":"2012","unstructured":"Asela Gunawardana, Guy Shani, and Sivan Yogev. 2012. Evaluating recommender systems. In Recommender Systems Handbook. Springer, 547\u2013601."},{"key":"e_1_3_3_52_2","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s10462-012-9364-9","article-title":"Shilling attacks against recommender systems: A comprehensive survey","volume":"42","author":"Gunes Ihsan","year":"2014","unstructured":"Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014. Shilling attacks against recommender systems: A comprehensive survey. Artif. Intell. Rev. 42, 4 (2014), 767\u2013799.","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"e_1_3_3_54_2","first-page":"355","volume-title":"SIGIR","author":"He Xiangnan","year":"2018","unstructured":"Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial personalized ranking for recommendation. In SIGIR. 355\u2013364."},{"key":"e_1_3_3_55_2","first-page":"1062","volume-title":"SIGKDD","author":"He Zhuangzhuang","year":"2024","unstructured":"Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun, Le Wu, Haoyue Bai, Jinqi Gong, Richang Hong, and Min Zhang. 2024. Double correction framework for denoising recommendation. In SIGKDD. 1062\u20131072."},{"key":"e_1_3_3_56_2","volume-title":"NeurIPS","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. 2019. Using self-supervised learning can improve model robustness and uncertainty. In NeurIPS, Vol. 32."},{"key":"e_1_3_3_57_2","first-page":"895","volume-title":"SIGKDD","author":"Hooi Bryan","year":"2016","unstructured":"Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos. 2016. Fraudar: Bounding graph fraud in the face of camouflage. In SIGKDD. 895\u2013904."},{"key":"e_1_3_3_58_2","first-page":"385","volume-title":"ICDE","author":"Hu Renjun","year":"2016","unstructured":"Renjun Hu, Charu C. Aggarwal, Shuai Ma, and Jinpeng Huai. 2016. An embedding approach to anomaly detection. In ICDE. IEEE, 385\u2013396."},{"key":"e_1_3_3_59_2","doi-asserted-by":"crossref","unstructured":"Peter J. Huber. 2011. Robust Statistics. In Int. Encycl. Stat. Sci. Miodrag Lovric (Ed.). Springer 1248\u20131251.","DOI":"10.1007\/978-3-642-04898-2_594"},{"issue":"2","key":"e_1_3_3_60_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","article-title":"The distribution of the flora in the alpine zone. 1","volume":"11","author":"Jaccard Paul","year":"1912","unstructured":"Paul Jaccard. 1912. The distribution of the flora in the alpine zone. 1. New Phytol. 11, 2 (1912), 37\u201350.","journal-title":"New Phytol."},{"key":"e_1_3_3_61_2","first-page":"19","volume-title":"SP","author":"Jagielski Matthew","year":"2018","unstructured":"Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. 2018. Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. In SP. IEEE, 19\u201335."},{"issue":"1","key":"e_1_3_3_62_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3390\/technologies9010002","article-title":"A survey on contrastive self-supervised learning","volume":"9","author":"Jaiswal Ashish","year":"2020","unstructured":"Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. 2020. A survey on contrastive self-supervised learning. Technologies 9, 1 (2020), 2.","journal-title":"Technologies"},{"key":"e_1_3_3_63_2","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511763113","volume-title":"Recommender Systems: An Introduction","author":"Jannach Dietmar","year":"2010","unstructured":"Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2010. Recommender Systems: An Introduction. Cambridge University Press, -."},{"key":"e_1_3_3_64_2","first-page":"4252","volume-title":"SIGKDD","author":"Jiang Yangqin","year":"2023","unstructured":"Yangqin Jiang, Chao Huang, and Lianghao Huang. 2023. Adaptive graph contrastive learning for recommendation. In SIGKDD. 4252\u20134261."},{"key":"e_1_3_3_65_2","first-page":"313","volume-title":"WSDM","author":"Jiang Yangqin","year":"2024","unstructured":"Yangqin Jiang, Yuhao Yang, Lianghao Xia, and Chao Huang. 2024. Diffkg: Knowledge graph diffusion model for recommendation. In WSDM. 313\u2013321."},{"key":"e_1_3_3_66_2","first-page":"197","volume-title":"ICDM","author":"Kang Wang-Cheng","year":"2018","unstructured":"Wang-Cheng Kang and Julian J. McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197\u2013206."},{"issue":"6","key":"e_1_3_3_67_2","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.ipm.2018.04.008","article-title":"News recommender systems\u2013Survey and roads ahead","volume":"54","author":"Karimi Mozhgan","year":"2018","unstructured":"Mozhgan Karimi, Dietmar Jannach, and Michael Jugovac. 2018. News recommender systems\u2013Survey and roads ahead. Inf. Process. Manag. 54, 6 (2018), 1203\u20131227.","journal-title":"Inf. Process. Manag."},{"key":"e_1_3_3_68_2","unstructured":"Maurice George Kendall. 1948. Rank Correlation Methods. Griffin."},{"key":"e_1_3_3_69_2","first-page":"5562","volume-title":"ICML","author":"Kim Hyunjik","year":"2021","unstructured":"Hyunjik Kim, George Papamakarios, and Andriy Mnih. 2021. The Lipschitz constant of self-attention. In ICML. PMLR, 5562\u20135571."},{"key":"e_1_3_3_70_2","first-page":"247","volume-title":"AAAI","author":"Kim Michael P.","year":"2019","unstructured":"Michael P. Kim, Amirata Ghorbani, and James Zou. 2019. Multiaccuracy: Black-box post-processing for fairness in classification. In AAAI. 247\u2013254."},{"issue":"8","key":"e_1_3_3_71_2","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren Yehuda","year":"2009","unstructured":"Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30\u201337.","journal-title":"Computer"},{"key":"e_1_3_3_72_2","first-page":"393","volume-title":"TheWebConf","author":"Lam Shyong K.","year":"2004","unstructured":"Shyong K. Lam and John Riedl. 2004. Shilling recommender systems for fun and profit. In TheWebConf. 393\u2013402."},{"issue":"7553","key":"e_1_3_3_73_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436\u2013444.","journal-title":"Nature"},{"key":"e_1_3_3_74_2","first-page":"656","volume-title":"SP","author":"Lecuyer Mathias","year":"2019","unstructured":"Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana. 2019. Certified robustness to adversarial examples with differential privacy. In SP. IEEE, 656\u2013672."},{"issue":"1","key":"e_1_3_3_75_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1287\/ijoc.1100.0440","article-title":"Shilling attack detection\u2013A new approach for a trustworthy recommender system","volume":"24","author":"Lee Jong-Seok","year":"2012","unstructured":"Jong-Seok Lee and Dan Zhu. 2012. Shilling attack detection\u2013A new approach for a trustworthy recommender system. INFORMS J. Comput. 24, 1 (2012), 117\u2013131.","journal-title":"INFORMS J. Comput."},{"key":"e_1_3_3_76_2","volume-title":"NeurIPS","author":"Li Bai","year":"2019","unstructured":"Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin. 2019. Certified adversarial robustness with additive noise. In NeurIPS, Vol. 32."},{"key":"e_1_3_3_77_2","first-page":"349","volume-title":"WSDM","author":"Li Ruirui","year":"2020","unstructured":"Ruirui Li, Xian Wu, and Wei Wang. 2020. Adversarial learning to compare: Self-attentive prospective customer recommendation in location based social networks. In WSDM. 349\u2013357."},{"key":"e_1_3_3_78_2","first-page":"811","volume-title":"CIKM","author":"Li Sheng","year":"2015","unstructured":"Sheng Li, Jaya Kawale, and Yun Fu. 2015. Deep collaborative filtering via marginalized denoising auto-encoder. In CIKM. 811\u2013820."},{"key":"e_1_3_3_79_2","first-page":"1346","volume-title":"CIKM","author":"Li Zongwei","year":"2024","unstructured":"Zongwei Li, Lianghao Xia, and Chao Huang. 2024. Recdiff: Diffusion model for social recommendation. In CIKM. 1346\u20131355."},{"key":"e_1_3_3_80_2","first-page":"1803","volume-title":"SIGKDD","author":"Liu Ninghao","year":"2018","unstructured":"Ninghao Liu, Hongxia Yang, and Xia Hu. 2018. Adversarial detection with model interpretation. In SIGKDD. ACM, 1803\u20131811."},{"issue":"1","key":"e_1_3_3_81_2","first-page":"857","article-title":"Self-supervised learning: Generative or contrastive","volume":"35","author":"Liu Xiao","year":"2021","unstructured":"Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. 2021. Self-supervised learning: Generative or contrastive. IEEE Trans. Knowl. Data Eng. 35, 1 (2021), 857\u2013876.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_3_82_2","first-page":"955","volume-title":"CIKM","author":"Liu Yuli","year":"2020","unstructured":"Yuli Liu. 2020. Recommending inferior results: A general and feature-free model for spam detection. In CIKM. 955\u2013974."},{"key":"e_1_3_3_83_2","first-page":"1099","volume-title":"CIKM","author":"Liu Yiyu","year":"2021","unstructured":"Yiyu Liu, Qian Liu, Yu Tian, Changping Wang, Yanan Niu, Yang Song, and Chenliang Li. 2021. Concept-aware denoising graph neural network for micro-video recommendation. In CIKM. 1099\u20131108."},{"key":"e_1_3_3_84_2","first-page":"419","volume-title":"SIGIR","author":"Liu Yang","year":"2020","unstructured":"Yang Liu, Xianzhuo Xia, Liang Chen, Xiangnan He, Carl Yang, and Zibin Zheng. 2020. Certifiable robustness to discrete adversarial perturbations for factorization machines. In SIGIR. 419\u2013428."},{"key":"e_1_3_3_85_2","unstructured":"Zhiwei Liu Yongjun Chen Jia Li Philip S. Yu Julian McAuley and Caiming Xiong. 2021. Contrastive self-supervised sequential recommendation with robust augmentation. arXiv preprint arXiv:2108.06479 (2021)."},{"key":"e_1_3_3_86_2","unstructured":"Zhuang Liu Yunpu Ma Yuanxin Ouyang and Zhang Xiong. 2021. Contrastive learning for recommender system. arXiv preprint arXiv:2101.01317 (2021)."},{"key":"e_1_3_3_87_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-85820-3_3","article-title":"Content-based recommender systems: State of the art and trends","author":"Lops Pasquale","year":"2011","unstructured":"Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). Springer, 73\u2013105.","journal-title":"Recommender Systems Handbook"},{"key":"e_1_3_3_88_2","first-page":"342","volume-title":"RecSys","author":"Minto Lorenzo","year":"2021","unstructured":"Lorenzo Minto, Moritz Haller, Benjamin Livshits, and Hamed Haddadi. 2021. Stronger privacy for federated collaborative filtering with implicit feedback. In RecSys. ACM, 342\u2013350."},{"key":"e_1_3_3_89_2","first-page":"7797","volume-title":"AAAI","author":"Mo Yujie","year":"2022","unstructured":"Yujie Mo, Liang Peng, Jie Xu, Xiaoshuang Shi, and Xiaofeng Zhu. 2022. Simple unsupervised graph representation learning. In AAAI, Vol. 36. 7797\u20137805."},{"issue":"4","key":"e_1_3_3_90_2","doi-asserted-by":"crossref","first-page":"23\u2013es","DOI":"10.1145\/1278366.1278372","article-title":"Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness","volume":"7","author":"Mobasher Bamshad","year":"2007","unstructured":"Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams. 2007. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7, 4 (2007), 23\u2013es.","journal-title":"ACM Trans. Internet Technol."},{"key":"e_1_3_3_91_2","first-page":"1","article-title":"Selective network discovery via deep reinforcement learning on embedded spaces","volume":"6","author":"Morales Peter","year":"2021","unstructured":"Peter Morales, Rajmonda Sulo Caceres, and Tina Eliassi-Rad. 2021. Selective network discovery via deep reinforcement learning on embedded spaces. Appl. Netw. Sci. 6, 1 (2021), 1\u201320.","journal-title":"Appl. Netw. Sci."},{"key":"e_1_3_3_92_2","volume-title":"NDSS","author":"Naseri Mohammad","year":"2022","unstructured":"Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro. 2022. Local and central differential privacy for robustness and privacy in federated learning. In NDSS."},{"key":"e_1_3_3_93_2","first-page":"188","volume-title":"EMNLP-IJCNLP","author":"Ni Jianmo","year":"2019","unstructured":"Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In EMNLP-IJCNLP. 188\u2013197."},{"key":"e_1_3_3_94_2","first-page":"1584","volume-title":"CIKM","author":"Oh Sejoon","year":"2022","unstructured":"Sejoon Oh, Berk Ustun, Julian McAuley, and Srijan Kumar. 2022. Rank list sensitivity of recommender systems to interaction perturbations. In CIKM. 1584\u20131594."},{"issue":"4","key":"e_1_3_3_95_2","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1145\/1031114.1031116","article-title":"Collaborative recommendation: A robustness analysis","volume":"4","author":"O\u2019Mahony Michael","year":"2004","unstructured":"Michael O\u2019Mahony, Neil Hurley, Nicholas Kushmerick, and Gu\u00e9nol\u00e9 Silvestre. 2004. Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol. 4, 4 (2004), 344\u2013377.","journal-title":"ACM Trans. Internet Technol."},{"key":"e_1_3_3_96_2","unstructured":"Aaron van den Oord Yazhe Li and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_3_97_2","doi-asserted-by":"publisher","DOI":"10.1145\/3494672"},{"key":"e_1_3_3_98_2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s11042-012-1119-8","article-title":"Preference-based user rating correction process for interactive recommendation systems","volume":"65","author":"Pham Hau Xuan","year":"2013","unstructured":"Hau Xuan Pham and Jason J. Jung. 2013. Preference-based user rating correction process for interactive recommendation systems. Multimed. Tools Appl. 65, 1 (2013), 119\u2013132.","journal-title":"Multimed. Tools Appl."},{"key":"e_1_3_3_99_2","first-page":"3320","volume-title":"STEP","author":"Pruksachatkun Yada","year":"2021","unstructured":"Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, and Kai-Wei Chang. 2021. Does robustness improve fairness? Approaching fairness with word substitution robustness methods for text classification. In STEP. 3320\u20133331."},{"key":"e_1_3_3_100_2","first-page":"859","volume-title":"SIGIR","author":"Qin Yuqi","year":"2021","unstructured":"Yuqi Qin, Pengfei Wang, and Chenliang Li. 2021. The world is binary: Contrastive learning for denoising next basket recommendation. In SIGIR. 859\u2013868."},{"issue":"4","key":"e_1_3_3_101_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3190616","article-title":"Sequence-aware recommender systems","volume":"51","author":"Quadrana Massimo","year":"2018","unstructured":"Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-aware recommender systems. ACM Comput. Surv. 51, 4 (2018), 1\u201336.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_102_2","first-page":"1097","volume-title":"TheWebConf","author":"Quan Yuhan","year":"2023","unstructured":"Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, and Yong Li. 2023. Robust preference-guided denoising for graph based social recommendation. In TheWebConf. 1097\u20131108."},{"key":"e_1_3_3_103_2","unstructured":"Steffen Rendle Christoph Freudenthaler Zeno Gantner and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452\u2013461."},{"key":"e_1_3_3_104_2","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718498"},{"key":"e_1_3_3_105_2","first-page":"364","volume-title":"SP","author":"Rezaei Shahbaz","year":"2023","unstructured":"Shahbaz Rezaei, Zubair Shafiq, and Xin Liu. 2023. Accuracy-privacy trade-off in deep ensemble: A membership inference perspective. In SP. IEEE, 364\u2013381."},{"key":"e_1_3_3_106_2","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1007\/s10462-020-09898-3","article-title":"A survey of attack detection approaches in collaborative filtering recommender systems","volume":"54","author":"Rezaimehr Fatemeh","year":"2021","unstructured":"Fatemeh Rezaimehr and Chitra Dadkhah. 2021. A survey of attack detection approaches in collaborative filtering recommender systems. Artif. Intell. Rev. 54, 3 (2021), 2011\u20132066.","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_3_107_2","unstructured":"Emanuele Rossi Ben Chamberlain Fabrizio Frasca Davide Eynard Federico Monti and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. In ICML Workshop on Graph Representation Learning."},{"key":"e_1_3_3_108_2","first-page":"378","volume-title":"RscSys","author":"Sato Masahiro","year":"2020","unstructured":"Masahiro Sato, Sho Takemori, Janmajay Singh, and Tomoko Ohkuma. 2020. Unbiased learning for the causal effect of recommendation. In RscSys. 378\u2013387."},{"issue":"13","key":"e_1_3_3_109_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3577925","article-title":"Self-supervised learning for videos: A survey","volume":"55","author":"Schiappa Madeline C.","year":"2023","unstructured":"Madeline C. Schiappa, Yogesh S. Rawat, and Mubarak Shah. 2023. Self-supervised learning for videos: A survey. ACM Comput. Surv. 55, 13s (2023), 1\u201337.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_110_2","volume-title":"NeurIPS","author":"Schmidt Ludwig","year":"2018","unstructured":"Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry. 2018. Adversarially robust generalization requires more data. In NeurIPS, Vol. 31."},{"key":"e_1_3_3_111_2","first-page":"111","volume-title":"TheWebConf","author":"Sedhain Suvash","year":"2015","unstructured":"Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In TheWebConf. 111\u2013112."},{"key":"e_1_3_3_112_2","first-page":"297","volume-title":"RecSys","author":"Seo Sungyong","year":"2017","unstructured":"Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In RecSys. 297\u2013305."},{"key":"e_1_3_3_113_2","doi-asserted-by":"crossref","unstructured":"Huawei Shen Yuanhao Liu Kaike Zhang Qi Cao and Xueqi Cheng. 2025. The rising safety concerns of deep recommender systems. The Innovation (2025) 101038.","DOI":"10.1016\/j.xinn.2025.101038"},{"key":"e_1_3_3_114_2","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1145\/3336191.3371831","volume-title":"WSDM","author":"Shenbin Ilya","year":"2020","unstructured":"Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I. Nikolenko. 2020. Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In WSDM. 528\u2013536."},{"issue":"9","key":"e_1_3_3_115_2","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1109\/TKDE.2018.2805356","article-title":"Privacy enhanced matrix factorization for recommendation with local differential privacy","volume":"30","author":"Shin Hyejin","year":"2018","unstructured":"Hyejin Shin, Sungwook Kim, Junbum Shin, and Xiaokui Xiao. 2018. Privacy enhanced matrix factorization for recommendation with local differential privacy. IEEE Trans. Knowl. Data Eng. 30, 9 (2018), 1770\u20131782.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_3_116_2","unstructured":"Anu Shrestha Francesca Spezzano and Maria Soledad Pera. 2021. An empirical analysis of collaborative recommender systems robustness to shilling attacks. In CEUR Workshop 3012 (2021) 45\u201357."},{"key":"e_1_3_3_117_2","first-page":"4934","volume-title":"AAAI","author":"Shriver David","year":"2019","unstructured":"David Shriver, Sebastian Elbaum, Matthew B. Dwyer, and David S. Rosenblum. 2019. Evaluating recommender system stability with influence-guided fuzzing. In AAAI, Vol. 33. 4934\u20134942."},{"issue":"3","key":"e_1_3_3_118_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MIC.2017.72","article-title":"Two decades of recommender systems at Amazon.com","volume":"21","author":"Smith Brent","year":"2017","unstructured":"Brent Smith and Greg Linden. 2017. Two decades of recommender systems at Amazon.com. IEEE Internet Comput. 21, 3 (2017), 12\u201318.","journal-title":"IEEE Internet Comput."},{"key":"e_1_3_3_119_2","volume-title":"NeurIPS workshop on machine learning for eCommerce","author":"Strub Florian","year":"2015","unstructured":"Florian Strub, Jeremie Mary, and Preux Philippe. 2015. Collaborative filtering with stacked denoising autoencoders and sparse inputs. In NeurIPS workshop on machine learning for eCommerce."},{"key":"e_1_3_3_120_2","first-page":"2806","volume-title":"SIGKDD","author":"Sun Youchen","year":"2024","unstructured":"Youchen Sun, Zhu Sun, Yingpeng Du, Jie Zhang, and Yew Soon Ong. 2024. Self-Supervised denoising through independent cascade graph augmentation for robust social recommendation. In SIGKDD. 2806\u20132817."},{"key":"e_1_3_3_121_2","volume-title":"Reinforcement Learning: An Introduction","author":"Sutton Richard S.","year":"2018","unstructured":"Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. MIT Press."},{"issue":"5","key":"e_1_3_3_122_2","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/TKDE.2019.2893638","article-title":"Adversarial training towards robust multimedia recommender system","volume":"32","author":"Tang Jinhui","year":"2019","unstructured":"Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, and Tat-Seng Chua. 2019. Adversarial training towards robust multimedia recommender system. IEEE Trans. Knowl. Data Eng. 32, 5 (2019), 855\u2013867.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_3_123_2","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1145\/3383313.3412243","volume-title":"RecSys","author":"Tang Jiaxi","year":"2020","unstructured":"Jiaxi Tang, Hongyi Wen, and Ke Wang. 2020. Revisiting adversarially learned injection attacks against recommender systems. In RecSys. 318\u2013327."},{"key":"e_1_3_3_124_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10462-017-9539-5","article-title":"Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning","volume":"50","author":"Tarus John K.","year":"2018","unstructured":"John K. Tarus, Zhendong Niu, and Ghulam Mustafa. 2018. Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning. Artif. Intell. Rev. 50, 1 (2018), 21\u201348.","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_3_125_2","unstructured":"OpenAI Team. 2022. ChatGPT: Optimizing language models for dialogue. Retrieved August 2023 from https:\/\/openai.com\/blog\/"},{"key":"e_1_3_3_126_2","first-page":"122","volume-title":"SIGIR","author":"Tian Changxin","year":"2022","unstructured":"Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, and Wayne Xin Zhao. 2022. Learning to denoise unreliable interactions for graph collaborative filtering. In SIGIR. 122\u2013132."},{"issue":"8","key":"e_1_3_3_127_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3551636","article-title":"A comprehensive survey on poisoning attacks and countermeasures in machine learning","volume":"55","author":"Tian Zhiyi","year":"2022","unstructured":"Zhiyi Tian, Lei Cui, Jie Liang, and Shui Yu. 2022. A comprehensive survey on poisoning attacks and countermeasures in machine learning. ACM Comput. Surv. 55, 8 (2022), 1\u201335.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_128_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.knosys.2014.12.011","article-title":"Correcting noisy ratings in collaborative recommender systems","volume":"76","author":"Toledo Raciel Yera","year":"2015","unstructured":"Raciel Yera Toledo, Yail\u00e9 Caballero Mota, and Luis Mart\u00ednez. 2015. Correcting noisy ratings in collaborative recommender systems. Knowl.-Based Syst. 76 (2015), 96\u2013108.","journal-title":"Knowl.-Based Syst."},{"key":"e_1_3_3_129_2","first-page":"838","volume-title":"FUSION","author":"Tomsett Richard","year":"2018","unstructured":"Richard Tomsett, Amy Widdicombe, Tianwei Xing, Supriyo Chakraborty, Simon Julier, Prudhvi Gurram, Raghuveer M. Rao, and Mani B. Srivastava. 2018. Why the failure? How adversarial examples can provide insights for interpretable machine learning. In FUSION. IEEE, 838\u2013845."},{"key":"e_1_3_3_130_2","first-page":"245","volume-title":"SIGIR","author":"Tran Thanh","year":"2019","unstructured":"Thanh Tran, Renee Sweeney, and Kyumin Lee. 2019. Adversarial Mahalanobis distance-based attentive song recommender for automatic playlist continuation. In SIGIR. 245\u2013254."},{"key":"e_1_3_3_131_2","volume-title":"ICLR","author":"Tsipras Dimitris","year":"2018","unstructured":"Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. 2018. Robustness may be at odds with accuracy. In ICLR."},{"issue":"7","key":"e_1_3_3_132_2","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.1109\/TKDE.2019.2960216","article-title":"Statistically robust evaluation of stream-based recommender systems","volume":"33","author":"Vinagre Joao","year":"2019","unstructured":"Joao Vinagre, Al\u00edpio M\u00e1rio Jorge, Concei\u00e7\u00e3o Rocha, and Joao Gama. 2019. Statistically robust evaluation of stream-based recommender systems. IEEE Trans. Knowl. Data Eng. 33, 7 (2019), 2971\u20132982.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_3_133_2","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1145\/1390156.1390294","volume-title":"ICML","author":"Vincent Pascal","year":"2008","unstructured":"Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In ICML. 1096\u20131103."},{"key":"e_1_3_3_134_2","first-page":"234","volume-title":"RecSys","author":"Wang Changsheng","year":"2023","unstructured":"Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, and Xiangnan He. 2023. Recad: Towards a unified library for recommender attack and defense. In RecSys. 234\u2013244."},{"key":"e_1_3_3_135_2","first-page":"7449","volume-title":"NeurIPS","author":"Wang Haotao","year":"2020","unstructured":"Haotao Wang, Tianlong Chen, Shupeng Gui, TingKuei Hu, Ji Liu, and Zhangyang Wang. 2020. Once-for-all adversarial training: In-situ tradeoff between robustness and accuracy for free. In NeurIPS, Vol. 33. 7449\u20137461."},{"key":"e_1_3_3_136_2","first-page":"535","volume-title":"WSDM","author":"Wang Hao","year":"2023","unstructured":"Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, and Zhiyuan Liu. 2023. Knowledge-adaptive contrastive learning for recommendation. In WSDM. 535\u2013543."},{"key":"e_1_3_3_137_2","doi-asserted-by":"crossref","unstructured":"Tianle Wang Lianghao Xia and Chao Huang. 2023. Denoised self-augmented learning for social recommendation. arXiv preprint arXiv:2305.12685 (2023).","DOI":"10.24963\/ijcai.2023\/258"},{"key":"e_1_3_3_138_2","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1145\/3437963.3441800","volume-title":"WSDM","author":"Wang Wenjie","year":"2021","unstructured":"Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021. Denoising implicit feedback for recommendation. In WSDM. 373\u2013381."},{"key":"e_1_3_3_139_2","doi-asserted-by":"crossref","unstructured":"Xuezhi Wang Haohan Wang and Diyi Yang. 2022. Measure and improve robustness in NLP models: A survey. In NAACL. ACL 4569\u20134586.","DOI":"10.18653\/v1\/2022.naacl-main.339"},{"key":"e_1_3_3_140_2","doi-asserted-by":"publisher","DOI":"10.1145\/3547333"},{"key":"e_1_3_3_141_2","first-page":"2015","volume-title":"TheWebConf","author":"Wang Yu","year":"2022","unstructured":"Yu Wang, Xin Xin, Zaiqiao Meng, Joemon M. Jose, Fuli Feng, and Xiangnan He. 2022. Learning robust recommenders through cross-model agreement. In TheWebConf. 2015\u20132025."},{"key":"e_1_3_3_142_2","first-page":"2502","volume-title":"SIGKDD","author":"Wang Zongwei","year":"2023","unstructured":"Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, Linxin Guo, and Hongzhi Yin. 2023. Efficient bi-level optimization for recommendation denoising. In SIGKDD. 2502\u20132511."},{"key":"e_1_3_3_143_2","first-page":"3070","volume-title":"MM","author":"Wang Zitai","year":"2021","unstructured":"Zitai Wang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. 2021. Implicit feedbacks are not always favorable: Iterative relabeled one-class collaborative filtering against noisy interactions. In MM. 3070\u20133078."},{"key":"e_1_3_3_144_2","first-page":"1074","volume-title":"SIGIR","author":"Wu Chenwang","year":"2021","unstructured":"Chenwang Wu, Defu Lian, Yong Ge, Zhihao Zhu, Enhong Chen, and Senchao Yuan. 2021. Fight fire with fire: Towards robust recommender systems via adversarial poisoning training. In SIGIR. 1074\u20131083."},{"key":"e_1_3_3_145_2","first-page":"3597","volume-title":"ACL","author":"Wu Fangzhao","year":"2020","unstructured":"Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, et\u00a0al. 2020. Mind: A large-scale dataset for news recommendation. In ACL. 3597\u20133606."},{"key":"e_1_3_3_146_2","first-page":"726","volume-title":"SIGIR","author":"Wu Jiancan","year":"2021","unstructured":"Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In SIGIR. 726\u2013735."},{"issue":"5","key":"e_1_3_3_147_2","first-page":"1","article-title":"Graph neural networks in recommender systems: A survey","volume":"55","author":"Wu Shiwen","year":"2022","unstructured":"Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022. Graph neural networks in recommender systems: A survey. ACM Comput. Surv. 55, 5 (2022), 1\u201337.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_148_2","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_3_3_149_2","first-page":"985","volume-title":"SIGKDD","author":"Wu Zhiang","year":"2012","unstructured":"Zhiang Wu, Junjie Wu, Jie Cao, and Dacheng Tao. 2012. HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In SIGKDD. 985\u2013993."},{"key":"e_1_3_3_150_2","first-page":"836","volume-title":"WSDM","author":"Xia Jiafeng","year":"2024","unstructured":"Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, and Ning Gu. 2024. Neural Kalman filtering for robust temporal recommendation. In WSDM. 836\u2013845."},{"key":"e_1_3_3_151_2","first-page":"1950","volume-title":"SIGIR","author":"Xie Sicong","year":"2022","unstructured":"Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen, and Wenliang Zhong. 2022. Denoising time cycle modeling for recommendation. In SIGIR. 1950\u20131955."},{"key":"e_1_3_3_152_2","first-page":"1259","volume-title":"ICDE","author":"Xie Xu","year":"2022","unstructured":"Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive learning for sequential recommendation. In ICDE. IEEE, 1259\u20131273."},{"key":"e_1_3_3_153_2","unstructured":"Da Xu Chuanwei Ruan Evren Korpeoglu Sushant Kumar and Kannan Achan. 2019. Self-attention with functional time representation learning. In NeurIPS 32 (2019)."},{"key":"e_1_3_3_154_2","first-page":"920","volume-title":"TheWebConf","author":"Xu Da","year":"2023","unstructured":"Da Xu, Tobias Schnabel, Xiquan Cui, Sarah Dean, Aniket Deshmukh, Bo Yang, and Shipeng Yu. 2023. Foreword for workshop on decision making for Inf. Retr. and recommender systems. In TheWebConf. 920\u2013920."},{"key":"e_1_3_3_155_2","first-page":"4173","volume-title":"SIGKDD","author":"Xu Jianpeng","year":"2021","unstructured":"Jianpeng Xu, Lingfei Wu, Xiaolin Pang, Mohit Sharma, Dawei Yin, George Karypis, Justin Basilico, and Philip S. Yu. 2021. 2nd international workshop on industrial recommendation systems (IRS). In SIGKDD. 4173\u20134174."},{"key":"e_1_3_3_156_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comcom.2013.06.009","article-title":"A survey of collaborative filtering based social recommender systems","volume":"41","author":"Yang Xiwang","year":"2014","unstructured":"Xiwang Yang, Yang Guo, Yong Liu, and Harald Steck. 2014. A survey of collaborative filtering based social recommender systems. Comput. Commun. 41 (2014), 1\u201310.","journal-title":"Comput. Commun."},{"key":"e_1_3_3_157_2","first-page":"1434","volume-title":"SIGIR","author":"Yang Yuhao","year":"2022","unstructured":"Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge graph contrastive learning for recommendation. In SIGIR. 1434\u20131443."},{"key":"e_1_3_3_158_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.052"},{"key":"e_1_3_3_159_2","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.knosys.2016.02.008","article-title":"Re-scale AdaBoost for attack detection in collaborative filtering recommender systems","volume":"100","author":"Yang Zhihai","year":"2016","unstructured":"Zhihai Yang, Lin Xu, Zhongmin Cai, and Zongben Xu. 2016. Re-scale AdaBoost for attack detection in collaborative filtering recommender systems. Knowl.-Based Syst. 100 (2016), 74\u201388.","journal-title":"Knowl.-Based Syst."},{"issue":"3","key":"e_1_3_3_160_2","first-page":"1","article-title":"Towards robust neural graph collaborative filtering via structure denoising and embedding perturbation","volume":"41","author":"Ye Haibo","year":"2023","unstructured":"Haibo Ye, Xinjie Li, Yuan Yao, and Hanghang Tong. 2023. Towards robust neural graph collaborative filtering via structure denoising and embedding perturbation. ACM Trans. Inf. Syst. 41, 3 (2023), 1\u201328.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_3_161_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.10.060"},{"key":"e_1_3_3_162_2","doi-asserted-by":"crossref","unstructured":"Junliang Yu Hongzhi Yin Xin Xia Tong Chen Jundong Li and Zi Huang. 2024. Self-supervised learning for recommender systems: A survey. IEEE TKDE 36 1 (2024) 335\u2013355.","DOI":"10.1109\/TKDE.2023.3282907"},{"key":"e_1_3_3_163_2","unstructured":"Yaodong Yu Zitong Yang Edgar Dobriban Jacob Steinhardt and Yi Ma. 2021. Understanding generalization in adversarial training via the bias-variance decomposition. arXiv preprint arXiv:2103.09947 (2021)."},{"key":"e_1_3_3_164_2","first-page":"1","volume-title":"IJCNN","author":"Yuan Feng","year":"2019","unstructured":"Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. Adversarial collaborative auto-encoder for top-n recommendation. In IJCNN. IEEE, 1\u20138."},{"key":"e_1_3_3_165_2","first-page":"1065","volume-title":"SIGIR","author":"Yuan Feng","year":"2019","unstructured":"Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. Adversarial collaborative neural network for robust recommendation. In SIGIR. 1065\u20131068."},{"key":"e_1_3_3_166_2","first-page":"1773","volume-title":"CIKM","author":"Yuan Feng","year":"2020","unstructured":"Feng Yuan, Lina Yao, and Boualem Benatallah. 2020. Exploring missing interactions: A convolutional generative adversarial network for collaborative filtering. In CIKM. 1773\u20131782."},{"issue":"9","key":"e_1_3_3_167_2","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","article-title":"Adversarial examples: Attacks and defenses for deep learning","volume":"30","author":"Yuan Xiaoyong","year":"2019","unstructured":"Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li. 2019. Adversarial examples: Attacks and defenses for deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30, 9 (2019), 2805\u20132824.","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"e_1_3_3_168_2","first-page":"59","volume-title":"RecSys","author":"Yue Zhenrui","year":"2022","unstructured":"Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, and Dong Wang. 2022. Defending substitution-based profile pollution attacks on sequential recommenders. In RecSys. 59\u201370."},{"issue":"8","key":"e_1_3_3_169_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3556536","article-title":"Evaluating recommender systems: Survey and framework","volume":"55","author":"Zangerle Eva","year":"2022","unstructured":"Eva Zangerle and Christine Bauer. 2022. Evaluating recommender systems: Survey and framework. ACM Comput. Surv. 55, 8 (2022), 1\u201338.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_170_2","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.knosys.2016.11.021","article-title":"Robust collaborative filtering based on non-negative matrix factorization and R1-norm","volume":"118","author":"Zhang Fuzhi","year":"2017","unstructured":"Fuzhi Zhang, Yuanli Lu, Jianmin Chen, Shaoshuai Liu, and Zhoujun Ling. 2017. Robust collaborative filtering based on non-negative matrix factorization and R1-norm. Knowl.-Based Syst. 118 (2017), 177\u2013190.","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"e_1_3_3_171_2","first-page":"226","article-title":"A meta-learning-based approach for detecting profile injection attacks in collaborative recommender systems","volume":"7","author":"Zhang Fuzhi","year":"2012","unstructured":"Fuzhi Zhang and Quanqiang Zhou. 2012. A meta-learning-based approach for detecting profile injection attacks in collaborative recommender systems. J. Comput. 7, 1 (2012), 226\u2013234.","journal-title":"J. Comput."},{"key":"e_1_3_3_172_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.knosys.2014.04.020","article-title":"HHT\u2013SVM: An online method for detecting profile injection attacks in collaborative recommender systems","volume":"65","author":"Zhang Fuzhi","year":"2014","unstructured":"Fuzhi Zhang and Quanqiang Zhou. 2014. HHT\u2013SVM: An online method for detecting profile injection attacks in collaborative recommender systems. Knowl.-Based Syst. 65 (2014), 96\u2013105.","journal-title":"Knowl.-Based Syst."},{"key":"e_1_3_3_173_2","first-page":"3309","volume-title":"SIGKDD","author":"Zhang Kaike","year":"2023","unstructured":"Kaike Zhang, Qi Cao, Gaolin Fang, Bingbing Xu, Hongjian Zou, Huawei Shen, and Xueqi Cheng. 2023. DyTed: Disentangled representation learning for discrete-time dynamic graph. In SIGKDD. 3309\u20133320."},{"key":"e_1_3_3_174_2","first-page":"680","volume-title":"RecSys","author":"Zhang Kaike","year":"2024","unstructured":"Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, and Xueqi Cheng. 2024. Improving the shortest plank: Vulnerability-aware adversarial training for robust recommender system. In RecSys. 680\u2013689."},{"key":"e_1_3_3_175_2","first-page":"1733","volume-title":"SIGIR","author":"Zhang Kaike","year":"2024","unstructured":"Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, and Xueqi Cheng. 2024. Lorec: Combating poisons with large language model for robust sequential recommendation. In SIGIR. 1733\u20131742."},{"key":"e_1_3_3_176_2","doi-asserted-by":"crossref","unstructured":"Kaike Zhang Qi Cao Yunfan Wu Fei Sun Huawei Shen and Xueqi Cheng. 2024. Lorec: Large language model for robust sequential recommendation against poisoning attacks. arXiv preprint arXiv:2401.17723 (2024).","DOI":"10.1145\/3626772.3657684"},{"key":"e_1_3_3_177_2","volume-title":"NeurIPS","author":"Zhang Kaike","year":"2024","unstructured":"Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, and Xueqi Cheng. 2024. Understanding and improving adversarial collaborative filtering for robust recommendation. In NeurIPS."},{"key":"e_1_3_3_178_2","volume-title":"TheWebConf","author":"Zhang Kaike","year":"2025","unstructured":"Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, and Xueqi Cheng. 2025. Personalized denoising implicit feedback for robust recommender system. In TheWebConf."},{"key":"e_1_3_3_179_2","doi-asserted-by":"crossref","unstructured":"Kaike Zhang Yunfan Wu Yougang Lyu Du Su Yingqiang Ge Shuchang Liu Qi Cao Zhaochun Ren and Fei Sun. 2025. The 1st workshop on human-centered recommender systems. In TheWebConf.","DOI":"10.1145\/3701716.3717736"},{"issue":"1","key":"e_1_3_3_180_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3285029","article-title":"Deep learning based recommender system: A survey and new perspectives","volume":"52","author":"Zhang Shuai","year":"2019","unstructured":"Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. 52, 1 (2019), 1\u201338.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_181_2","first-page":"689","volume-title":"SIGIR","author":"Zhang Shijie","year":"2020","unstructured":"Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, and Lizhen Cui. 2020. GCN-based user representation learning for unifying robust recommendation and fraudster detection. In SIGIR. 689\u2013698."},{"key":"e_1_3_3_182_2","first-page":"3229","volume-title":"TheWebConf","author":"Zhang Wei","year":"2022","unstructured":"Wei Zhang, Junbing Yan, Zhuo Wang, and Jianyong Wang. 2022. Neuro-symbolic interpretable collaborative filtering for attribute-based recommendation. In TheWebConf. 3229\u20133238."},{"issue":"1","key":"e_1_3_3_183_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/1500000066","article-title":"Explainable recommendation: A survey and new perspectives","volume":"14","year":"2020","unstructured":"Yongfeng Zhang and Xu Chen. 2020. Explainable recommendation: A survey and new perspectives. Found. Trends Inf. Retr. 14, 1 (2020), 1\u2013101.","journal-title":"Found. Trends Inf. Retr."},{"key":"e_1_3_3_184_2","first-page":"2677","volume-title":"SIGIR","author":"Zhang Yongfeng","year":"2021","unstructured":"Yongfeng Zhang, Xu Chen, Yi Zhang, and Xianjie Chen. 2021. CSR 2021: The 1st international workshop on causality in search and recommendation. In SIGIR. 2677\u20132680."},{"key":"e_1_3_3_185_2","volume-title":"IJCAI","author":"Zhang Yongfeng","year":"2015","unstructured":"Yongfeng Zhang, Yunzhi Tan, Min Zhang, Yiqun Liu, Tat-Seng Chua, and Shaoping Ma. 2015. Catch the black sheep: Unified framework for shilling attack detection based on fraudulent action propagation. In IJCAI."},{"issue":"7","key":"e_1_3_3_186_2","first-page":"3169","article-title":"Deep pairwise hashing for cold-start recommendation","volume":"34","author":"Zhang Yan","year":"2020","unstructured":"Yan Zhang, Ivor W. Tsang, Hongzhi Yin, Guowu Yang, Defu Lian, and Jingjing Li. 2020. Deep pairwise hashing for cold-start recommendation. IEEE Trans. Knowl. Data Eng. 34, 7 (2020), 3169\u20133181.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5","key":"e_1_3_3_187_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3178115","article-title":"Deep learning for environmentally robust speech recognition: An overview of recent developments","volume":"9","author":"Zhang Zixing","year":"2018","unstructured":"Zixing Zhang, J\u00fcrgen Geiger, Jouni Pohjalainen, Amr El-Desoky Mousa, Wenyu Jin, and Bj\u00f6rn Schuller. 2018. Deep learning for environmentally robust speech recognition: An overview of recent developments. ACM Trans. Intell. Syst. Technol. 9, 5 (2018), 1\u201328.","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"e_1_3_3_188_2","first-page":"1","volume-title":"FUSION","author":"Zhang Zhuo","year":"2014","unstructured":"Zhuo Zhang and Sanjeev R. Kulkarni. 2014. Detection of shilling attacks in recommender systems via spectral clustering. In FUSION. IEEE, 1\u20138."},{"key":"e_1_3_3_189_2","first-page":"4722","volume-title":"CIKM","author":"Zhao Wayne Xin","year":"2022","unstructured":"Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, et\u00a0al. 2022. Recbole 2.0: Towards a more up-to-date recommendation library. In CIKM. 4722\u20134726."},{"key":"e_1_3_3_190_2","first-page":"2338","volume-title":"SIGKDD","author":"Zheng Jiawei","year":"2021","unstructured":"Jiawei Zheng, Qianli Ma, Hao Gu, and Zhenjing Zheng. 2021. Multi-view denoising graph auto-encoders on heterogeneous information networks for cold-start recommendation. In SIGKDD. 2338\u20132348."},{"key":"e_1_3_3_191_2","first-page":"1893","volume-title":"CIKM","author":"Zhou Kun","year":"2020","unstructured":"Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020. S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM. 1893\u20131902."},{"key":"e_1_3_3_192_2","first-page":"955","volume-title":"SIGIR","author":"Zhou Wei","year":"2014","unstructured":"Wei Zhou, Yun Sing Koh, Junhao Wen, Shafiq Alam, and Gillian Dobbie. 2014. Detection of abnormal profiles on group attacks in recommender systems. In SIGIR. 955\u2013958."},{"key":"e_1_3_3_193_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130968"},{"key":"e_1_3_3_194_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.137"},{"key":"e_1_3_3_195_2","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2020. Deep graph contrastive representation learning. In ICML Workshop on Graph Representation Learning."},{"key":"e_1_3_3_196_2","first-page":"1837","volume-title":"CIKM","author":"Zou Jun","year":"2013","unstructured":"Jun Zou and Faramarz Fekri. 2013. A belief propagation approach for detecting shilling attacks in collaborative filtering. In CIKM. 1837\u20131840."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3757057","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T12:53:15Z","timestamp":1756731195000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3757057"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":195,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,31]]}},"alternative-id":["10.1145\/3757057"],"URL":"https:\/\/doi.org\/10.1145\/3757057","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"2023-09-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}