{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:44:15Z","timestamp":1787017455833,"version":"build-2736575974"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272437"],"award-info":[{"award-number":["62272437"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Recommendation unlearning is an emerging task to serve users for erasing unusable data (e.g., some historical behaviors) from a well-trained recommender model. Existing methods process unlearning requests by fully or partially retraining the model after removing the unusable data. However, these methods are impractical due to the high computation cost of full retraining and the highly possible performance damage of partial training. In this light, a desired recommendation unlearning method should obtain a similar model as full retraining in a more efficient manner, i.e., achieving complete, efficient and harmless unlearning.<\/jats:p>\n                  <jats:p>\n                    In this work, we propose a new\n                    <jats:italic>Influence Function-based Recommendation Unlearning<\/jats:italic>\n                    (IFRU) framework, which efficiently updates the model without retraining by estimating the influence of the unusable data on the model via the\n                    <jats:italic>influence function<\/jats:italic>\n                    . In the light that recent recommender models use historical data for both the constructions of the optimization loss and the computational graph (e.g., neighborhood aggregation), IFRU jointly estimates the direct influence of unusable data on optimization loss and the spillover influence on the computational graph to pursue complete unlearning. Furthermore, we propose an importance-based pruning algorithm to reduce the cost of the influence function. IFRU is harmless and applicable to mainstream differentiable models. Extensive experiments demonstrate that IFRU achieves more than 250 times acceleration compared to retraining-based methods with recommendation performance comparable to full retraining. Codes are available at https:\/\/github.com\/baiyimeng\/IFRU.\n                  <\/jats:p>","DOI":"10.1145\/3701763","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T06:11:48Z","timestamp":1730182308000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Recommendation Unlearning via Influence Function"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7863-5183","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9092-8888","authenticated-orcid":false,"given":"Zhiyu","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8874-9409","authenticated-orcid":false,"given":"Yimeng","family":"Bai","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6941-5218","authenticated-orcid":false,"given":"Jiancan","family":"Wu","sequence":"additional","affiliation":[{"name":"Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7570-5756","authenticated-orcid":false,"given":"Qifan","family":"Wang","sequence":"additional","affiliation":[{"name":"Meta AI, Meta Platforms Inc, Menlo Park, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-9842","authenticated-orcid":false,"given":"Fuli","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"17953","article-title":"If influence functions are the answer, then what is the question?","volume":"35","author":"Bae Juhan","year":"2022","unstructured":"Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, and Roger B. Grosse. 2022. If influence functions are the answer, then what is the question? Advances in Neural Information Processing Systems 35 (2022), 17953\u201317967.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_3_2","first-page":"1007","volume-title":"Proceedings of the 17th ACM Conference on Recommender Systems","author":"Bao Keqin","year":"2023","unstructured":"Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems. 1007\u20131014."},{"key":"e_1_3_2_4_2","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"Basu Samyadeep","year":"2021","unstructured":"Samyadeep Basu, Phillip Pope, and Soheil Feizi. 2021. Influence functions in deep learning are fragile. In Proceedings of the 9th International Conference on Learning Representations."},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/SP40001.2021.00019","volume-title":"Proceedings of the 2021 IEEE Symposium on Security and Privacy (SP)","author":"Bourtoule Lucas","year":"2021","unstructured":"Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021. Machine unlearning. In Proceedings of the 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 141\u2013159."},{"key":"e_1_3_2_6_2","first-page":"463","volume-title":"Proceedings of the 2015 IEEE Symposium on Security and Privacy","author":"Cao Yinzhi","year":"2015","unstructured":"Yinzhi Cao and Junfeng Yang. 2015. Towards making systems forget with machine unlearning. In Proceedings of the 2015 IEEE Symposium on Security and Privacy. IEEE, 463\u2013480."},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1145\/3485447.3511997","volume-title":"Proceedings of the ACM Web Conference 2022","author":"Chen Chong","year":"2022","unstructured":"Chong Chen, Fei Sun, Min Zhang, and Bolin Ding. 2022. Recommendation unlearning. In Proceedings of the ACM Web Conference 2022. 2768\u20132777."},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1145\/3548606.3559352","volume-title":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security","author":"Chen Min","year":"2022","unstructured":"Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang. 2022. Graph unlearning. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security. 499\u2013513."},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1145\/3292500.3330857","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Cheng Weiyu","year":"2019","unstructured":"Weiyu Cheng, Yanyan Shen, Linpeng Huang, and Yanmin Zhu. 2019. Incorporating interpretability into latent factor models via fast influence analysis. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 885\u2013893."},{"key":"e_1_3_2_10_2","volume-title":"Residuals and Influence in Regression","author":"Cook R. Dennis","year":"1982","unstructured":"R. Dennis Cook and Sanford Weisberg. 1982. Residuals and Influence in Regression. New York: Chapman and Hall."},{"issue":"1","key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3426723","article-title":"Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations","volume":"39","author":"Fang Hui","year":"2020","unstructured":"Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo. 2020. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems 39, 1 (2020), 1\u201342.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1145\/3366423.3380072","volume-title":"Proceedings of the Web Conference 2020","author":"Fang Minghong","year":"2020","unstructured":"Minghong Fang, Neil Zhenqiang Gong, and Jia Liu. 2020. Influence function based data poisoning attacks to top-n recommender systems. In Proceedings of the Web Conference 2020. 3019\u20133025."},{"issue":"8","key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett Tom","year":"2006","unstructured":"Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27, 8 (2006), 861\u2013874.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_2_14_2","first-page":"2881","article-title":"What neural networks memorize and why: Discovering the long tail via influence estimation","volume":"33","author":"Feldman Vitaly","year":"2020","unstructured":"Vitaly Feldman and Chiyuan Zhang. 2020. What neural networks memorize and why: Discovering the long tail via influence estimation. Advances in Neural Information Processing Systems 33 (2020), 2881\u20132891.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"346","key":"e_1_3_2_15_2","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/01621459.1974.10482962","article-title":"The influence curve and its role in robust estimation","volume":"69","author":"Hampel Frank R.","year":"1974","unstructured":"Frank R. Hampel. 1974. The influence curve and its role in robust estimation. Journal of the American Statistical Association 69, 346 (1974), 383\u2013393.","journal-title":"Journal of the American Statistical Association"},{"key":"e_1_3_2_16_2","first-page":"507","volume-title":"Proceedings of the 25th International Conference on World Wide Web","author":"He Ruining","year":"2016","unstructured":"Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web. 507\u2013517."},{"key":"e_1_3_2_17_2","first-page":"639","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 639\u2013648."},{"key":"e_1_3_2_18_2","first-page":"173","volume-title":"Proceedings of the 26th International Conference on World Wide Web","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. 173\u2013182."},{"key":"e_1_3_2_19_2","unstructured":"Zhiyu Hu Yang Zhang Minghao Xiao Wenjie Wang Fuli Feng and Xiangnan He. 2024. Exact and efficient unlearning for large language model-based recommendation. arXiv:2404.10327. Retrieved from https:\/\/arxiv.org\/abs\/2404.10327"},{"key":"e_1_3_2_20_2","first-page":"793","volume-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference","author":"Huang Yangsibo","year":"2021","unstructured":"Yangsibo Huang, Xiaoxiao Li, and Kai Li. 2021. Ema: Auditing data removal from trained models. In Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference. Springer, 793\u2013803."},{"key":"e_1_3_2_21_2","first-page":"186:1\u2013186:27","volume-title":"Proceedings of the CHI \u201921: CHI Conference on Human Factors in Computing Systems","author":"Juneja Prerna","year":"2021","unstructured":"Prerna Juneja and Tanushree Mitra. 2021. Auditing e-commerce platforms for algorithmically curated vaccine misinformation. In Proceedings of the CHI \u201921: CHI Conference on Human Factors in Computing Systems. ACM, 186:1\u2013186:27."},{"key":"e_1_3_2_22_2","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations (ICLR\u201915). San Diego CA USA May 7-9 2015 Conference Track Proceedings."},{"key":"e_1_3_2_23_2","first-page":"1885","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Koh Pang Wei","year":"2017","unstructured":"Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In Proceedings of the International Conference on Machine Learning. PMLR, 1885\u20131894."},{"issue":"8","key":"e_1_3_2_24_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_2_25_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/978-1-0716-2197-4_3","article-title":"Advances in collaborative filtering","author":"Koren Yehuda","year":"2022","unstructured":"Yehuda Koren, Steffen Rendle, and Robert Bell. 2022. Advances in collaborative filtering. Recommender Systems Handbook (2022), 91\u2013142.","journal-title":"Recommender Systems Handbook"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"111124","DOI":"10.1016\/j.knosys.2023.111124","article-title":"Making recommender systems forget: Learning and unlearning for erasable recommendation","volume":"283","author":"Li Yuyuan","year":"2024","unstructured":"Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Junlin Liu, and Jun Wang. 2024. Making recommender systems forget: Learning and unlearning for erasable recommendation. Knowledge-Based Systems 283, C (2024), 111124.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"121025","DOI":"10.1016\/j.eswa.2023.121025","article-title":"Selective and collaborative influence function for efficient recommendation unlearning","volume":"234","author":"Li Yuyuan","year":"2023","unstructured":"Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, Jun Wang, and Linxun Chen. 2023. Selective and collaborative influence function for efficient recommendation unlearning. Expert Systems with Applications 234 (2023), 121025.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_2_28_2","unstructured":"Jianghao Lin Xinyi Dai Yunjia Xi Weiwen Liu Bo Chen Hao Zhang Yong Liu Chuhan Wu Xiangyang Li Chenxu Zhu Huifeng Guo Yong Yu Ruiming Tang and Weinan Zhang. 2024. How can recommender systems benefit from large language models: A survey. ACM Trans. Inf. Syst. Just Accepted (July 2024)."},{"key":"e_1_3_2_29_2","unstructured":"Wenyan Liu Juncheng Wan Xiaoling Wang Weinan Zhang Dell Zhang and Hang Li. 2022. Forgetting fast in recommender systems. arXiv:2208.06875. Retrieved from https:\/\/arxiv.org\/abs\/2208.06875"},{"issue":"3","key":"e_1_3_2_30_2","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.clsr.2013.03.010","article-title":"The EU proposal for a general data protection regulation and the roots of the \u2018right to be forgotten\u2019","volume":"29","author":"Mantelero Alessandro","year":"2013","unstructured":"Alessandro Mantelero. 2013. The EU proposal for a general data protection regulation and the roots of the \u2018right to be forgotten\u2019. Computer Law and Security Review 29, 3 (2013), 229\u2013235.","journal-title":"Computer Law and Security Review"},{"key":"e_1_3_2_31_2","first-page":"7691","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Marchant Neil G.","year":"2022","unstructured":"Neil G. Marchant, Benjamin I. P. Rubinstein, and Scott Alfeld. 2022. Hard to forget: Poisoning attacks on certified machine unlearning. In Proceedings of the AAAI Conference on Artificial Intelligence. 7691\u20137700."},{"key":"e_1_3_2_32_2","first-page":"6631","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Martinez Javier Abad","year":"2023","unstructured":"Javier Abad Martinez, Umang Bhatt, Adrian Weller, and Giovanni Cherubin. 2023. Approximating full conformal prediction at scale via influence functions. In Proceedings of the AAAI Conference on Artificial Intelligence. 6631\u20136639."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00146-020-00950-y"},{"key":"e_1_3_2_34_2","unstructured":"Thanh Tam Nguyen Thanh Trung Huynh Phi Le Nguyen Alan Wee-Chung Liew Hongzhi Yin and Quoc Viet Hung Nguyen. 2022. A survey of machine unlearning. arXiv:2209.02299. Retrieved from https:\/\/arxiv.org\/abs\/2209.02299"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.18653\/v1\/2023.acl-short.104","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2023","author":"Nguyen-Duc Thang","year":"2023","unstructured":"Thang Nguyen-Duc, Hoang Thanh-Tung, Quan Hung Tran, Dang Huu-Tien, Hieu Nguyen, Anh T. V. Dau, and Nghi Bui. 2023. Class based influence functions for error detection. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2023. 1204\u20131218."},{"issue":"1","key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.2478\/acss-2019-0003","article-title":"Modern approaches to building recommender systems for online stores","volume":"24","author":"Onokoy Lyudmila","year":"2019","unstructured":"Lyudmila Onokoy and Jurijs Lavendels. 2019. Modern approaches to building recommender systems for online stores. Applied Computer Systems 24, 1 (2019), 18\u201324.","journal-title":"Applied Computer Systems"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/ICDM.2010.127","volume-title":"Proceedings of the 2010 IEEE International Conference on Data Mining","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle. 2010. Factorization machines. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, 995\u20131000."},{"key":"e_1_3_2_38_2","first-page":"452","volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452\u2013461."},{"issue":"44035","key":"e_1_3_2_39_2","first-page":"46992","article-title":"Amnesia-a selection of machine learning models that can forget user data very fast","volume":"8364","author":"Schelter Sebastian","year":"2020","unstructured":"Sebastian Schelter. 2020. Amnesia-a selection of machine learning models that can forget user data very fast. Suicide 8364, 44035 (2020), 46992.","journal-title":"Suicide"},{"key":"e_1_3_2_40_2","first-page":"1589","volume-title":"Proceedings of the 29th USENIX Security Symposium (USENIX Security 20)","author":"Shan Shawn","year":"2020","unstructured":"Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y Zhao. 2020. Fawkes: Protecting privacy against unauthorized deep learning models. In Proceedings of the 29th USENIX Security Symposium (USENIX Security 20). 1589\u20131604."},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1145\/2043932.2043987","volume-title":"Proceedings of the 5th ACM Conference on Recommender Systems","author":"Tak\u00e1cs G\u00e1bor","year":"2011","unstructured":"G\u00e1bor Tak\u00e1cs, Istv\u00e1n Pil\u00e1szy, and Domonkos Tikk. 2011. Applications of the conjugate gradient method for implicit feedback collaborative filtering. In Proceedings of the 5th ACM Conference on Recommender Systems. 297\u2013300."},{"key":"e_1_3_2_42_2","article-title":"Fast yet effective machine unlearning","author":"Tarun Ayush K.","year":"2023","unstructured":"Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, and Mohan Kankanhalli. 2023. Fast yet effective machine unlearning. IEEE Transactions on Neural Networks and Learning Systems 35, 9 (2023), 13046\u201313055.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/EuroSP53844.2022.00027","volume-title":"Proceedings of the 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)","author":"Thudi Anvith","year":"2022","unstructured":"Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot. 2022. Unrolling sgd: Understanding factors influencing machine unlearning. In Proceedings of the 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P). IEEE, 303\u2013319."},{"key":"e_1_3_2_44_2","first-page":"4007","volume-title":"Proceedings of the 31st USENIX Security Symposium (USENIX Security 22)","author":"Thudi Anvith","year":"2022","unstructured":"Anvith Thudi, Hengrui Jia, Ilia Shumailov, and Nicolas Papernot. 2022. On the necessity of auditable algorithmic definitions for machine unlearning. In Proceedings of the 31st USENIX Security Symposium (USENIX Security 22). 4007\u20134022."},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1145\/3523227.3551473","volume-title":"Proceedings of the 16th ACM Conference on Recommender Systems","author":"Tommasel Antonela","year":"2022","unstructured":"Antonela Tommasel and Filippo Menczer. 2022. Do recommender systems make social media more susceptible to misinformation spreaders?. In Proceedings of the 16th ACM Conference on Recommender Systems. Association for Computing Machinery, 550\u2013555."},{"key":"e_1_3_2_46_2","volume-title":"Proceedings of the Network and Distributed System Security Symposium (NDSS) 2023","author":"Warnecke Alexander","year":"2023","unstructured":"Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck. 2023. Machine unlearning of features and labels. In Proceedings of the Network and Distributed System Security Symposium (NDSS) 2023."},{"key":"e_1_3_2_47_2","first-page":"1830","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Wu Chenwang","year":"2021","unstructured":"Chenwang Wu, Defu Lian, Yong Ge, Zhihao Zhu, and Enhong Chen. 2021. Triple adversarial learning for influence based poisoning attack in recommender systems. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1830\u20131840."},{"key":"e_1_3_2_48_2","first-page":"8675","volume-title":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","author":"Wu Ga","year":"2022","unstructured":"Ga Wu, Masoud Hashemi, and Christopher Srinivasa. 2022. PUMA: Performance unchanged model augmentation for training data removal. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI Press, 8675\u20138682."},{"key":"e_1_3_2_49_2","first-page":"651-\u2013661","volume-title":"Proceedings of the ACM Web Conference 2023","author":"Wu Jiancan","year":"2023","unstructured":"Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, and Xiangnan He. 2023. GIF: A general graph unlearning strategy via influence function. In Proceedings of the ACM Web Conference 2023. 651-\u2013661."},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Likang Wu Zhi Zheng Zhaopeng Qiu Hao Wang Hongchao Gu Tingjia Shen Chuan Qin Chen Zhu Hengshu Zhu Qi Liu Hui Xiong and Enhong Chen. 2024. A survey on large language models for recommendation. World Wide Web 27 5 (Sep. 2024).","DOI":"10.1007\/s11280-024-01291-2"},{"key":"e_1_3_2_51_2","article-title":"Machine unlearning: A survey","author":"Xu Heng","year":"2023","unstructured":"Heng Xu, Tianqing Zhu*, Lefeng Zhang, Wanlei Zhou, and Philip S. Yu. 2023. Machine unlearning: A survey. ACM Computing Surveys 56, 1 (2023), 1\u201336.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_52_2","unstructured":"Mimee Xu Jiankai Sun Xin Yang Kevin Yao and Chong Wang. 2023. Netflix and forget: Efficient and exact machine unlearning from bi-linear recommendations. arXiv:2302.06676. Retrieved from https:\/\/arxiv.org\/abs\/2302.06676"},{"issue":"9","key":"e_1_3_2_53_2","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/CC.2014.6969776","article-title":"A robust collaborative recommendation algorithm based on k-distance and tukey m-estimator","volume":"11","author":"Yi Huawei","year":"2014","unstructured":"Huawei Yi, Fuzhi Zhang, and Jie Lan. 2014. A robust collaborative recommendation algorithm based on k-distance and tukey m-estimator. China Communications 11, 9 (2014), 112\u2013123.","journal-title":"China Communications"},{"key":"e_1_3_2_54_2","first-page":"1929","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yu Jiangxing","year":"2020","unstructured":"Jiangxing Yu, Hong Zhu, Chih-Yao Chang, Xinhua Feng, Bowen Yuan, Xiuqiang He, and Zhenhua Dong. 2020. Influence function for unbiased recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1929\u20131932."},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","unstructured":"Wei Yuan Hongzhi Yin Fangzhao Wu Shijie Zhang Tieke He and Hao Wang. 2023. Federated unlearning for on-device recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM\u201923). Association for Computing Machinery New York NY USA 393\u2013401.","DOI":"10.1145\/3539597.3570463"},{"key":"e_1_3_2_56_2","volume-title":"Proceedings of the 10th International Conference on Learning Representations","author":"Zeng Yi","year":"2022","unstructured":"Yi Zeng, Si Chen, Won Park, Zhuoqing Mao, Ming Jin, and Ruoxi Jia. 2022. Adversarial unlearning of backdoors via implicit hypergradient. In Proceedings of the 10th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1145\/3366423.3379992","volume-title":"Proceedings of the Web Conference 2020","author":"Zhang Hengtong","year":"2020","unstructured":"Hengtong Zhang, Yaliang Li, Bolin Ding, and Jing Gao. 2020. Practical data poisoning attack against next-item recommendation. In Proceedings of the Web Conference 2020. 2458\u20132464."},{"issue":"1","key":"e_1_3_2_58_2","first-page":"5:1\u20135:38","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 Computing Surveys 52, 1 (2019), 5:1\u20135:38.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_59_2","first-page":"5399","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Zhang Wei","year":"2021","unstructured":"Wei Zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, and Fan Zhang. 2021. On sample based explanation methods for NLP: Faithfulness, efficiency and semantic evaluation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 5399\u20135411."},{"key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1145\/3397271.3401167","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhang Yang","year":"2020","unstructured":"Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, and Yongdong Zhang. 2020. How to retrain recommender system? A sequential meta-learning method. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1479\u20131488."},{"key":"e_1_3_2_61_2","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhang Yang","year":"2023","unstructured":"Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, and Yongdong Zhang. 2023. Reformulating CTR prediction: Learning invariant feature interactions for recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval."}],"container-title":["ACM Transactions on Recommender Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701763","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701763","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:57:16Z","timestamp":1750283836000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":60,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6,30]]}},"alternative-id":["10.1145\/3701763"],"URL":"https:\/\/doi.org\/10.1145\/3701763","relation":{},"ISSN":["2770-6699"],"issn-type":[{"value":"2770-6699","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,23]]},"assertion":[{"value":"2023-09-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}