{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:11:59Z","timestamp":1770523919744,"version":"3.49.0"},"reference-count":102,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022ZD0114804"],"award-info":[{"award-number":["2022ZD0114804"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0102"],"award-info":[{"award-number":["2021SHZDZX0102"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62177033"],"award-info":[{"award-number":["62177033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61921006"],"award-info":[{"award-number":["61921006"]}],"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. Inf. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have achieved better performance in terms of user engagement metrics such as clicks and browsing time. The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption. A natural following question is whether current recommendation algorithms are manipulating user preferences. If so, can we measure the manipulation level? In this article, we present a general framework for benchmarking the degree of manipulations of recommendation algorithms, in both slate recommendation and sequential recommendation scenarios. The framework consists of four stages, initial preference calculation, training data collection, algorithm training and interaction, and metrics calculation that involves two proposed metrics, Manipulation Score and Preference Shift. We benchmark some representative recommendation algorithms in both synthetic and real-world datasets under the proposed framework. We have observed that a high online click-through rate does not necessarily mean a better understanding of user initial preference, but ends in prompting users to choose more documents they initially did not favor. Moreover, we find that the training data have notable impacts on the manipulation degrees, and algorithms with more powerful modeling abilities are more sensitive to such impacts. The experiments also verified the usefulness of the proposed metrics for measuring the degree of manipulations. We advocate that future recommendation algorithm studies should be treated as an optimization problem with constrained user preference manipulations.<\/jats:p>","DOI":"10.1145\/3637869","type":"journal-article","created":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T06:58:22Z","timestamp":1702709902000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9310-3598","authenticated-orcid":false,"given":"Zhengbang","family":"Zhu","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9319-4497","authenticated-orcid":false,"given":"Rongjun","family":"Qin","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University, China and Polixir Technologies, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5637-0735","authenticated-orcid":false,"given":"Junjie","family":"Huang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3351-5401","authenticated-orcid":false,"given":"Xinyi","family":"Dai","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1052-5447","authenticated-orcid":false,"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software Technology, Nanjing University, China and Polixir Technologies, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0281-8271","authenticated-orcid":false,"given":"Yong","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0127-2425","authenticated-orcid":false,"given":"Weinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"issue":"1","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1287\/isre.2017.0703","article-title":"Effects of online recommendations on consumers\u2019 willingness to pay","volume":"29","author":"Adomavicius Gediminas","year":"2018","unstructured":"Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, and Jingjing Zhang. 2018. Effects of online recommendations on consumers\u2019 willingness to pay. Information Systems Research 29, 1 (2018), 84\u2013102.","journal-title":"Information Systems Research"},{"key":"e_1_3_1_3_2","first-page":"135","volume-title":"Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Ai Qingyao","year":"2018","unstructured":"Qingyao Ai, Keping Bi, Jiafeng Guo, and W Bruce Croft. 2018. Learning a deep listwise context model for ranking refinement. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 135\u2013144."},{"issue":"1","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.econlet.2009.12.028","article-title":"Anchoring and cognitive ability","volume":"107","author":"Bergman Oscar","year":"2010","unstructured":"Oscar Bergman, Tore Ellingsen, Magnus Johannesson, and Cicek Svensson. 2010. Anchoring and cognitive ability. Economics Letters 107, 1 (2010), 66\u201368.","journal-title":"Economics Letters"},{"key":"e_1_3_1_5_2","unstructured":"Lucas Bernardi Sakshi Batra and Cintia Alicia Bruscantini. 2021. Simulations in recommender systems: An industry perspective. CoRR abs\/2109.06723 (2021). arXiv:2109.06723 https:\/\/arxiv.org\/abs\/2109.06723"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1145\/3292500.3330745","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Beutel Alex","year":"2019","unstructured":"Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, et\u00a0al. 2019. Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2212\u20132220."},{"key":"e_1_3_1_7_2","first-page":"46","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"Beutel Alex","year":"2018","unstructured":"Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, and Ed H. Chi. 2018. Latent cross: Making use of context in recurrent recommender systems. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 46\u201354."},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1145\/2872427.2883033","volume-title":"Proceedings of the 25th International Conference on World Wide Web","author":"Borisov Alexey","year":"2016","unstructured":"Alexey Borisov, Ilya Markov, Maarten De Rijke, and Pavel Serdyukov. 2016. A neural click model for web search. In Proceedings of the 25th International Conference on World Wide Web. 531\u2013541."},{"key":"e_1_3_1_9_2","first-page":"45","volume-title":"Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Borisov Alexey","year":"2018","unstructured":"Alexey Borisov, Martijn Wardenaar, Ilya Markov, and Maarten de Rijke. 2018. A click sequence model for web search. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 45\u201354."},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1145\/1102351.1102363","volume-title":"Proceedings of the 22nd International Conference on Machine Learning","author":"Burges Chris","year":"2005","unstructured":"Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to rank using gradient descent. In Proceedings of the 22nd International Conference on Machine Learning. 89\u201396."},{"issue":"23","key":"e_1_3_1_11_2","first-page":"81","article-title":"From ranknet to lambdarank to lambdamart: An overview","volume":"11","author":"Burges Christopher J. C.","year":"2010","unstructured":"Christopher J. C. Burges. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning 11, 23-581 (2010), 81.","journal-title":"Learning"},{"issue":"19","key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"10575","DOI":"10.1073\/pnas.96.19.10575","article-title":"Behavioral economics: Reunifying psychology and economics","volume":"96","author":"Camerer Colin","year":"1999","unstructured":"Colin Camerer. 1999. Behavioral economics: Reunifying psychology and economics. Proceedings of the National Academy of Sciences 96, 19 (1999), 10575\u201310577.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Micah D. Carroll Anca Dragan Stuart Russell and Dylan Hadfield-Menell. 2022. Estimating and penalizing induced preference shifts in recommender systems. In International Conference on Machine Learning. PMLR 2686\u20132708.","DOI":"10.1145\/3460231.3478849"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1145\/3240323.3240370","volume-title":"Proceedings of the 12th ACM Conference on Recommender Systems","author":"Chaney Allison J. B.","year":"2018","unstructured":"Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In Proceedings of the 12th ACM Conference on Recommender Systems. 224\u2013232."},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geb.2017.02.015","article-title":"Confirmation bias with motivated beliefs","volume":"104","author":"Charness Gary","year":"2017","unstructured":"Gary Charness and Chetan Dave. 2017. Confirmation bias with motivated beliefs. Games and Economic Behavior 104 (2017), 1\u201323. https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0899825617300416","journal-title":"Games and Economic Behavior"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1145\/3357384.3358158","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Chen Jia","year":"2019","unstructured":"Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2019. TianGong-ST: A new dataset with large-scale refined real-world web search sessions. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2485\u20132488."},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1145\/3336191.3371819","volume-title":"Proceedings of the 13th International Conference on Web Search and Data Mining","author":"Chen Jia","year":"2020","unstructured":"Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2020. A context-aware click model for web search. In Proceedings of the 13th International Conference on Web Search and Data Mining. 88\u201396."},{"key":"e_1_3_1_18_2","first-page":"1","volume-title":"Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data","author":"Chen Qiwei","year":"2019","unstructured":"Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, and Wenwu Ou. 2019. Behavior sequence transformer for e-commerce recommendation in alibaba. In Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. 1\u20134."},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1145\/2009916.2010051","volume-title":"Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Chen Ye","year":"2011","unstructured":"Ye Chen and John F. Canny. 2011. Recommending ephemeral items at web scale. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1013\u20131022."},{"issue":"2","key":"e_1_3_1_20_2","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.im.2018.09.002","article-title":"How do product recommendations affect impulse buying? An empirical study on WeChat social commerce","volume":"56","author":"Chen Yanhong","year":"2019","unstructured":"Yanhong Chen, Yaobin Lu, Bin Wang, and Zhao Pan. 2019. How do product recommendations affect impulse buying? An empirical study on WeChat social commerce. Information and Management 56, 2 (2019), 236\u2013248.","journal-title":"Information and Management"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir Rohan Anil Zakaria Haque Lichan Hong Vihan Jain Xiaobing Liu and Hemal Shah. 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 7\u201310.","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_1_22_2","unstructured":"Kyunghyun Cho Bart van Merrienboer Dzmitry Bahdanau and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. In Proceedings of SSST@EMNLP 2014 Eighth Workshop on Syntax Semantics and Structure in Statistical Translation. Association for Computational Linguistics 103\u2013111."},{"issue":"11","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1016\/j.jbusres.2014.02.010","article-title":"Random regret minimization for consumer choice modeling: Assessment of empirical evidence","volume":"67","author":"Chorus Caspar","year":"2014","unstructured":"Caspar Chorus, Sander van Cranenburgh, and Thijs Dekker. 2014. Random regret minimization for consumer choice modeling: Assessment of empirical evidence. Journal of Business Research 67, 11 (2014), 2428\u20132436.","journal-title":"Journal of Business Research"},{"key":"e_1_3_1_24_2","first-page":"87","volume-title":"Proceedings of the 2008 International Conference on Web Search and Data Mining","author":"Craswell Nick","year":"2008","unstructured":"Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An experimental comparison of click position-bias models. In Proceedings of the 2008 International Conference on Web Search and Data Mining. 87\u201394."},{"key":"e_1_3_1_25_2","first-page":"1809","volume-title":"Proceedings of the Web Conference 2021","author":"Dai Xinyi","year":"2021","unstructured":"Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, and Yong Yu. 2021. An adversarial imitation click model for information retrieval. In Proceedings of the Web Conference 2021. 1809\u20131820."},{"key":"e_1_3_1_26_2","first-page":"271","volume-title":"Proceedings of the 16th International Conference on World Wide Web","author":"Das Abhinandan S.","year":"2007","unstructured":"Abhinandan S. Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web. 271\u2013280."},{"key":"e_1_3_1_27_2","first-page":"172","volume-title":"Proceedings of the Conference on Fairness, Accountability and Transparency","author":"Ekstrand Michael D.","year":"2018","unstructured":"Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness. In Proceedings of the Conference on Fairness, Accountability and Transparency. PMLR, 172\u2013186."},{"key":"e_1_3_1_28_2","unstructured":"Fenglei Fan Jinjun Xiong and Ge Wang. 2020. On Interpretability of artificial neural networks. CoRR abs\/2001.02522 (2020). arXiv:2001.02522 http:\/\/arxiv.org\/abs\/2001.02522"},{"key":"e_1_3_1_29_2","first-page":"2052","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Fujimoto Scott","year":"2019","unstructured":"Scott Fujimoto, David Meger, and Doina Precup. 2019. Off-policy deep reinforcement learning without exploration. In Proceedings of the International Conference on Machine Learning. PMLR, 2052\u20132062."},{"issue":"1","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.socec.2010.10.008","article-title":"A literature review of the anchoring effect","volume":"40","author":"Furnham Adrian","year":"2011","unstructured":"Adrian Furnham and Hua Chu Boo. 2011. A literature review of the anchoring effect. The Journal of Socio-economics 40, 1 (2011), 35\u201342.","journal-title":"The Journal of Socio-economics"},{"key":"e_1_3_1_31_2","unstructured":"Diksha Garg Priyanka Gupta Pankaj Malhotra Lovekesh Vig and Gautam Shroff. 2020. Batch-constrained distributional reinforcement learning for session-based recommendation. CoRR abs\/2012.08984 (2020). arXiv:2012.08984 https:\/\/arxiv.org\/abs\/2012.08984"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"Nada Ghanem Stephan Leitner and Dietmar Jannach. 2022. Balancing consumer and business value of recommender systems: A simulation-based analysis. Electronic Commerce Research and Applications 55 (2022) 101195.","DOI":"10.1016\/j.elerap.2022.101195"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.trb.2016.07.012","article-title":"Modeling the decoy effect with context-RUM Models: Diagrammatic analysis and empirical evidence from route choice SP and mode choice RP case studies","volume":"93","author":"Guevara C. Angelo","year":"2016","unstructured":"C. Angelo Guevara and Mitsuyoshi Fukushi. 2016. Modeling the decoy effect with context-RUM Models: Diagrammatic analysis and empirical evidence from route choice SP and mode choice RP case studies. Transportation Research Part B: Methodological 93 (2016), 318\u2013337. https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0191261516301345","journal-title":"Transportation Research Part B: Methodological"},{"key":"e_1_3_1_34_2","first-page":"11","volume-title":"Proceedings of the 18th International Conference on World Wide Web","author":"Guo Fan","year":"2009","unstructured":"Fan Guo, Chao Liu, Anitha Kannan, Tom Minka, Michael Taylor, Yi-Min Wang, and Christos Faloutsos. 2009. Click chain model in web search. In Proceedings of the 18th International Conference on World Wide Web. 11\u201320."},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 1725\u20131731.","DOI":"10.24963\/ijcai.2017\/239"},{"key":"e_1_3_1_36_2","first-page":"309","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems","author":"He Ruining","year":"2016","unstructured":"Ruining He, Chen Fang, Zhaowen Wang, and Julian McAuley. 2016. Vista: A visually, socially, and temporally-aware model for artistic recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. 309\u2013316."},{"key":"e_1_3_1_37_2","first-page":"191","volume-title":"Proceedings of the 2016 IEEE 16th International Conference on Data Mining","author":"He Ruining","year":"2016","unstructured":"Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining. IEEE, 191\u2013200."},{"key":"e_1_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Xiangnan He Xiaoyu Du Xiang Wang Feng Tian Jinhui Tang and Tat-Seng Chua. 2018. Outer product-based neural collaborative filtering. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. 2227\u20132233.","DOI":"10.24963\/ijcai.2018\/308"},{"key":"e_1_3_1_39_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_1_40_2","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1145\/3269206.3271761","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","author":"Hidasi Bal\u00e1zs","year":"2018","unstructured":"Bal\u00e1zs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 843\u2013852."},{"key":"e_1_3_1_41_2","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In 4th International Conference on Learning Representations."},{"issue":"1","key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1086\/208899","article-title":"Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis","volume":"9","author":"Huber Joel","year":"1982","unstructured":"Joel Huber, John W. Payne, and Christopher Puto. 1982. Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis. Journal of Consumer Research 9, 1 (1982), 90\u201398.","journal-title":"Journal of Consumer Research"},{"key":"e_1_3_1_43_2","unstructured":"Eugene Ie Chih-Wei Hsu Martin Mladenov Vihan Jain Sanmit Narvekar Jing Wang Rui Wu and Craig Boutilier. 2019. RecSim: A configurable simulation platform for recommender systems. CoRR abs\/1909.04847 (2019). arXiv:1909.04847 http:\/\/arxiv.org\/abs\/1909.04847"},{"key":"e_1_3_1_44_2","doi-asserted-by":"crossref","unstructured":"Eugene Ie Vihan Jain Jing Wang Sanmit Narvekar Ritesh Agarwal Rui Wu Heng-Tze Cheng Tushar Chandra and Craig Boutilier. 2019. SlateQ: A tractable decomposition for reinforcement learning with recommendation sets. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 2592\u20132599.","DOI":"10.24963\/ijcai.2019\/360"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1145\/3306618.3314288","volume-title":"Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society","author":"Jiang Ray","year":"2019","unstructured":"Ray Jiang, Silvia Chiappa, Tor Lattimore, Andr\u00e1s Gy\u00f6rgy, and Pushmeet Kohli. 2019. Degenerate feedback loops in recommender systems. In Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society. 383\u2013390."},{"key":"e_1_3_1_46_2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1145\/3018661.3018719","volume-title":"Proceedings of the 10th ACM International Conference on Web Search and Data Mining","author":"Jing How","year":"2017","unstructured":"How Jing and Alexander J. Smola. 2017. Neural survival recommender. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. 515\u2013524."},{"key":"e_1_3_1_47_2","unstructured":"Michael I. Jordan. 2003. An introduction to probabilistic graphical models. MIT Press."},{"issue":"1","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1177\/004728759503400106","article-title":"Consumer choice in context: The decoy effect in travel and tourism","volume":"34","author":"Josiam Bharath M.","year":"1995","unstructured":"Bharath M. Josiam and J. S. Perry Hobson. 1995. Consumer choice in context: The decoy effect in travel and tourism. Journal of Travel Research 34, 1 (1995), 45\u201350.","journal-title":"Journal of Travel Research"},{"key":"e_1_3_1_49_2","first-page":"197","volume-title":"Proceedings of the 2018 IEEE International Conference on Data Mining","author":"Kang Wang-Cheng","year":"2018","unstructured":"Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the 2018 IEEE International Conference on Data Mining. IEEE, 197\u2013206."},{"key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1145\/1401890.1401944","volume-title":"Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Koren Yehuda","year":"2008","unstructured":"Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 426\u2013434."},{"issue":"4","key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MPRV.2008.85","article-title":"User-generated content","volume":"7","author":"Krumm John","year":"2008","unstructured":"John Krumm, Nigel Davies, and Chandra Narayanaswami. 2008. User-generated content. IEEE Pervasive Computing 7, 4 (2008), 10\u201311.","journal-title":"IEEE Pervasive Computing"},{"key":"e_1_3_1_52_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/SLT.2006.326774","volume-title":"Proceedings of the 2006 IEEE Spoken Language Technology Workshop","author":"Lemon Oliver","year":"2006","unstructured":"Oliver Lemon, Kallirroi Georgila, and James Henderson. 2006. Evaluating effectiveness and portability of reinforcement learned dialogue strategies with real users: The TALK TownInfo evaluation. In Proceedings of the 2006 IEEE Spoken Language Technology Workshop. IEEE, 178\u2013181."},{"issue":"1","key":"e_1_3_1_53_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/89.817450","article-title":"A stochastic model of human-machine interaction for learning dialog strategies","volume":"8","author":"Levin Esther","year":"2000","unstructured":"Esther Levin, Roberto Pieraccini, and Wieland Eckert. 2000. A stochastic model of human-machine interaction for learning dialog strategies. IEEE Transactions on Speech and Audio Processing 8, 1 (2000), 11\u201323.","journal-title":"IEEE Transactions on Speech and Audio Processing"},{"key":"e_1_3_1_54_2","unstructured":"Elisabeth Lex Mario Wagner and Dominik Kowald. 2018. Mitigating confirmation bias on twitter by recommending opposing views. CoRR abs\/1809.03901 (2018). arXiv:1809.03901 http:\/\/arxiv.org\/abs\/1809.03901"},{"issue":"7","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1016\/j.eswa.2013.11.020","article-title":"Modeling and broadening temporal user interest in personalized news recommendation","volume":"41","author":"Li Lei","year":"2014","unstructured":"Lei Li, Li Zheng, Fan Yang, and Tao Li. 2014. Modeling and broadening temporal user interest in personalized news recommendation. Expert Systems with Applications 41, 7 (2014), 3168\u20133177.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_1_56_2","first-page":"3020","volume-title":"Proceedings of the World Wide Web Conference","author":"Liu Shang","year":"2019","unstructured":"Shang Liu, Zhenzhong Chen, Hongyi Liu, and Xinghai Hu. 2019. User-video co-attention network for personalized micro-video recommendation. In Proceedings of the World Wide Web Conference. 3020\u20133026."},{"key":"e_1_3_1_57_2","first-page":"2145","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Mansoury Masoud","year":"2020","unstructured":"Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2020. Feedback loop and bias amplification in recommender systems. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 2145\u20132148."},{"issue":"4","key":"e_1_3_1_58_2","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1007\/s00146-020-00950-y","article-title":"Recommender systems and their ethical challenges","volume":"35","author":"Milano Silvia","year":"2020","unstructured":"Silvia Milano, Mariarosaria Taddeo, and Luciano Floridi. 2020. Recommender systems and their ethical challenges. Ai and Society 35, 4 (2020), 957\u2013967.","journal-title":"Ai and Society"},{"key":"e_1_3_1_59_2","volume-title":"Consumer Response to Product Unavailability","author":"Min Kyeong Sam","year":"2003","unstructured":"Kyeong Sam Min. 2003. Consumer Response to Product Unavailability. The Ohio State University."},{"key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1145\/3383313.3411527","volume-title":"Proceedings of the 14th ACM Conference on Recommender Systems","author":"Mladenov Martin","year":"2020","unstructured":"Martin Mladenov, Chih-wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, and Craig Boutilier. 2020. Demonstrating principled uncertainty modeling for recommender ecosystems with RecSim NG. In Proceedings of the 14th ACM Conference on Recommender Systems. 591\u2013593."},{"key":"e_1_3_1_61_2","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra and Martin A. Riedmiller. 2013. Playing atari with deep reinforcement learning. CoRR abs\/1312.5602 (2013). arXiv:1312.5602 http:\/\/arxiv.org\/abs\/1312.5602"},{"key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1145\/2566486.2568012","volume-title":"Proceedings of the 23rd International Conference on World Wide Web","author":"Nguyen Tien T.","year":"2014","unstructured":"Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014. Exploring the filter bubble: The effect of using recommender systems on content diversity. In Proceedings of the 23rd International Conference on World Wide Web. 677\u2013686."},{"issue":"2","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1037\/1089-2680.2.2.175","article-title":"Confirmation bias: A ubiquitous phenomenon in many guises","volume":"2","author":"Nickerson Raymond S.","year":"1998","unstructured":"Raymond S. Nickerson. 1998. Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology 2, 2 (1998), 175\u2013220.","journal-title":"Review of General Psychology"},{"key":"e_1_3_1_64_2","doi-asserted-by":"crossref","unstructured":"Xiao Pan Lei Wu Fenjie Long and Ma Ang. 2022. Exploiting user behavior learning for personalized trajectory recommendations. Frontiers of Computer Science 16 3 (2022) 1\u201312.","DOI":"10.1007\/s11704-020-0243-2"},{"key":"e_1_3_1_65_2","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1145\/3397271.3401104","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Pang Liang","year":"2020","unstructured":"Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, and Jirong Wen. 2020. Setrank: Learning a permutation-invariant ranking model for information retrieval. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 499\u2013508."},{"key":"e_1_3_1_66_2","first-page":"211","volume-title":"Proceedings of the 2017 11th International Conference on Research Challenges in Information Science","author":"Paraschakis Dimitris","year":"2017","unstructured":"Dimitris Paraschakis. 2017. Towards an ethical recommendation framework. In Proceedings of the 2017 11th International Conference on Research Challenges in Information Science. IEEE, 211\u2013220."},{"key":"e_1_3_1_67_2","doi-asserted-by":"crossref","unstructured":"Changhua Pei Yi Zhang Yongfeng Zhang Fei Sun Xiao Lin Hanxiao Sun Jian Wu Peng Jiang Junfeng Ge Wenwu Ou and Dan Pei. 2019. Personalized re-ranking for recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems. 3\u201311.","DOI":"10.1145\/3298689.3347000"},{"issue":"2","key":"e_1_3_1_68_2","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1109\/TSA.2005.855836","article-title":"A probabilistic framework for dialog simulation and optimal strategy learning","volume":"14","author":"Pietquin Olivier","year":"2006","unstructured":"Olivier Pietquin and Thierry Dutoit. 2006. A probabilistic framework for dialog simulation and optimal strategy learning. IEEE Transactions on Audio, Speech, and Language Processing 14, 2 (2006), 589\u2013599.","journal-title":"IEEE Transactions on Audio, Speech, and Language Processing"},{"key":"e_1_3_1_69_2","first-page":"2347","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Qin Jiarui","year":"2020","unstructured":"Jiarui Qin, Weinan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, and Yong Yu. 2020. User behavior retrieval for click-through rate prediction. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2347\u20132356."},{"key":"e_1_3_1_70_2","doi-asserted-by":"crossref","unstructured":"Kan Ren Jiarui Qin Yuchen Fang Weinan Zhang Lei Zheng Weijie Bian Guorui Zhou Jian Xu Yong Yu Xiaoqiang Zhu and Kun Gai. 2019. Lifelong sequential modeling with personalized memorization for user response prediction. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 565\u2013574.","DOI":"10.1145\/3331184.3331230"},{"key":"e_1_3_1_71_2","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1145\/1772690.1772773","volume-title":"Proceedings of the 19th International Conference on World Wide Web","author":"Rendle Steffen","year":"2010","unstructured":"Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. 811\u2013820."},{"key":"e_1_3_1_72_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1145\/3351095.3372879","volume-title":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","author":"Ribeiro Manoel Horta","year":"2020","unstructured":"Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virg\u00edlio A. F. Almeida, and Wagner Meira Jr. 2020. Auditing radicalization pathways on YouTube. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 131\u2013141."},{"key":"e_1_3_1_73_2","unstructured":"David Rohde Stephen Bonner Travis Dunlop Flavian Vasile and Alexandros Karatzoglou. 2018. RecoGym: A reinforcement learning environment for the problem of product recommendation in online advertising. CoRR abs\/1808.00720 (2018). arXiv:1808.00720 http:\/\/arxiv.org\/abs\/1808.00720"},{"key":"e_1_3_1_74_2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1145\/371920.372071","volume-title":"Proceedings of the 10th International Conference on World Wide Web","author":"Sarwar Badrul","year":"2001","unstructured":"Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. 285\u2013295."},{"issue":"2","key":"e_1_3_1_75_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1017\/S0269888906000944","article-title":"A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies","volume":"21","author":"Schatzmann Jost","year":"2006","unstructured":"Jost Schatzmann, Karl Weilhammer, Matt Stuttle, and Steve Young. 2006. A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. The Knowledge Engineering Review 21, 2 (2006), 97\u2013126.","journal-title":"The Knowledge Engineering Review"},{"key":"e_1_3_1_76_2","unstructured":"Paul Schoemaker. 1982. The expected utility model: Its variants purposes evidence and limitations. Journal of Economic Literature 20 2 (1982) 529\u2013563."},{"key":"e_1_3_1_77_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. CoRR abs\/1707.06347 (2017). arXiv:1707.06347 http:\/\/arxiv.org\/abs\/1707.06347"},{"issue":"4","key":"e_1_3_1_78_2","first-page":"649","article-title":"Bounded rationality","volume":"146","author":"Selten Reinhard","year":"1990","unstructured":"Reinhard Selten. 1990. Bounded rationality. Journal of Institutional and Theoretical Economics (JITE)\/Zeitschrift f\u00fcr die Gesamte Staatswissenschaft 146, 4 (1990), 649\u2013658.","journal-title":"Journal of Institutional and Theoretical Economics (JITE)\/Zeitschrift f\u00fcr die Gesamte Staatswissenschaft"},{"key":"e_1_3_1_79_2","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1145\/3292500.3330933","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Shang Wenjie","year":"2019","unstructured":"Wenjie Shang, Yang Yu, Qingyang Li, Zhiwei Qin, Yiping Meng, and Jieping Ye. 2019. Environment reconstruction with hidden confounders for reinforcement learning based recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 566\u2013576."},{"key":"e_1_3_1_80_2","first-page":"491","volume-title":"Proceedings of the 13th ACM Conference on Recommender Systems","author":"Shi Bichen","year":"2019","unstructured":"Bichen Shi, Makbule Gulcin Ozsoy, Neil Hurley, Barry Smyth, Elias Z. Tragos, James Geraci, and Aonghus Lawlor. 2019. Pyrecgym: A reinforcement learning gym for recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems. 491\u2013495."},{"key":"e_1_3_1_81_2","first-page":"4902","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Shi Jing-Cheng","year":"2019","unstructured":"Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and An-Xiang Zeng. 2019. Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4902\u20134909."},{"issue":"5","key":"e_1_3_1_82_2","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1037\/0021-9010.84.5.823","article-title":"Decoy effects and attribute-level inferences.","volume":"84","author":"Slaughter Jerel E.","year":"1999","unstructured":"Jerel E. Slaughter, Evan F. Sinar, and Scott Highhouse. 1999. Decoy effects and attribute-level inferences. Journal of Applied Psychology 84, 5 (1999), 823.","journal-title":"Journal of Applied Psychology"},{"key":"e_1_3_1_83_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1145\/1835804.1835835","volume-title":"Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Srikant Ramakrishnan","year":"2010","unstructured":"Ramakrishnan Srikant, Sugato Basu, Ni Wang, and Daryl Pregibon. 2010. User browsing models: Relevance versus examination. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 223\u2013232."},{"key":"e_1_3_1_84_2","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1145\/3357384.3357895","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Sun Fei","year":"2019","unstructured":"Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1441\u20131450."},{"key":"e_1_3_1_85_2","first-page":"565","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"Tang Jiaxi","year":"2018","unstructured":"Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 565\u2013573."},{"key":"e_1_3_1_86_2","first-page":"1","volume-title":"Proceedings of the Workshop Decisions@ RecSys, in Conjunction with the Fourth ACM Conference on Recommender Systems","author":"Teppan Erich","year":"2011","unstructured":"Erich Teppan, Alexander Felfernig, and Klaus Isak. 2011. Decoy effects in financial service e-sales systems. In Proceedings of the Workshop Decisions@ RecSys, in Conjunction with the Fourth ACM Conference on Recommender Systems. Citeseer, 1\u20138."},{"key":"e_1_3_1_87_2","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1145\/3343413.3378004","volume-title":"Proceedings of the 2020 Conference on Human Information Interaction and Retrieval","author":"Tian Mucun","year":"2020","unstructured":"Mucun Tian and Michael D. Ekstrand. 2020. Estimating error and bias in offline evaluation results. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. 392\u2013396."},{"key":"e_1_3_1_88_2","first-page":"1835","volume-title":"Proceedings of the 2018 World Wide Web Conference","author":"Wang Hongwei","year":"2018","unstructured":"Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 World Wide Web Conference. 1835\u20131844."},{"key":"e_1_3_1_89_2","doi-asserted-by":"crossref","unstructured":"Kai Wang Zhene Zou Minghao Zhao Qilin Deng Yue Shang Yile Liang Runze Wu Xudong Shen Tangjie Lyu and Changjie Fan. 2023. RL4RS: A real-world dataset for reinforcement learning based recommender system. In Proceedings of the 46th InternationalACM SIGIR Conference on Research and Development in Information Retrieval. 2935\u20132944.","DOI":"10.1145\/3539618.3591899"},{"key":"e_1_3_1_90_2","first-page":"133","volume-title":"Proceedings of the 15th ACM Conference on Recommender Systems","author":"Wang Ningxia","year":"2021","unstructured":"Ningxia Wang and Li Chen. 2021. User bias in beyond-accuracy measurement of recommendation algorithms. In Proceedings of the 15th ACM Conference on Recommender Systems. 133\u2013142."},{"key":"e_1_3_1_91_2","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1145\/3269206.3271784","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","author":"Wang Xuanhui","year":"2018","unstructured":"Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, and Marc Najork. 2018. The lambdaloss framework for ranking metric optimization. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1313\u20131322."},{"key":"e_1_3_1_92_2","doi-asserted-by":"crossref","unstructured":"Yifan Wang Weizhi Ma Min Zhang Yiqun Liu and Shaoping Ma. 2023. A Survey on the Fairness of Recommender Systems. ACM Transactions on Information Systems 41 3 (2023) 1\u201343.","DOI":"10.1145\/3547333"},{"issue":"4","key":"e_1_3_1_93_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1852102.1852106","article-title":"A similarity measure for indefinite rankings","volume":"28","author":"Webber William","year":"2010","unstructured":"William Webber, Alistair Moffat, and Justin Zobel. 2010. A similarity measure for indefinite rankings. ACM Transactions on Information Systems 28, 4 (2010), 1\u201338.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_94_2","first-page":"1192","volume-title":"Proceedings of the 25th International Conference on Machine Learning","author":"Xia Fen","year":"2008","unstructured":"Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008. Listwise approach to learning to rank: Theory and algorithm. In Proceedings of the 25th International Conference on Machine Learning. 1192\u20131199."},{"key":"e_1_3_1_95_2","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence","author":"Xiao Teng","year":"2021","unstructured":"Teng Xiao and Donglin Wang. 2021. A general offline reinforcement learning framework for interactive recommendation. In Proceedings of the 35th AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_1_96_2","unstructured":"Sirui Yao Yoni Halpern Nithum Thain Xuezhi Wang Kang Lee Flavien Prost Ed H. Chi Jilin Chen and Alex Beutel. 2021. Measuring recommender system effects with simulated users. CoRR abs\/2101.04526 (2021). arXiv:2101.04526 https:\/\/arxiv.org\/abs\/2101.04526"},{"key":"e_1_3_1_97_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1145\/2983323.2983758","volume-title":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","author":"Yuan Fajie","year":"2016","unstructured":"Fajie Yuan, Guibing Guo, Joemon M Jose, Long Chen, Haitao Yu, and Weinan Zhang. 2016. Lambdafm: Learning optimal ranking with factorization machines using lambda surrogates. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 227\u2013236."},{"key":"e_1_3_1_98_2","doi-asserted-by":"crossref","unstructured":"Yu Zhang Peter Ti\u0148o Ale\u0161 Leonardis and Ke Tang. 2021. A survey on neural network interpretability. IEEE Transactions on Emerging Topics in Computational Intelligence 5 5 (2021) 726\u2013742.","DOI":"10.1109\/TETCI.2021.3100641"},{"key":"e_1_3_1_99_2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1145\/3298689.3346997","volume-title":"Proceedings of the 13th ACM Conference on Recommender Systems","author":"Zhao Zhe","year":"2019","unstructured":"Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending what video to watch next: A multitask ranking system. In Proceedings of the 13th ACM Conference on Recommender Systems. 43\u201351."},{"key":"e_1_3_1_100_2","first-page":"5941","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhou Guorui","year":"2019","unstructured":"Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence. 5941\u20135948."},{"key":"e_1_3_1_101_2","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1145\/3219819.3219823","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Zhou Guorui","year":"2018","unstructured":"Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1059\u20131068."},{"key":"e_1_3_1_102_2","first-page":"85","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Zhu Ziwei","year":"2021","unstructured":"Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee. 2021. Popularity-opportunity bias in collaborative filtering. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 85\u201393."},{"key":"e_1_3_1_103_2","first-page":"449","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Zhu Ziwei","year":"2020","unstructured":"Ziwei Zhu, Jianling Wang, and James Caverlee. 2020. Measuring and mitigating item under-recommendation bias in personalized ranking systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 449\u2013458."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637869","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637869","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:18Z","timestamp":1750286958000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,9]]},"references-count":102,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3637869"],"URL":"https:\/\/doi.org\/10.1145\/3637869","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,9]]},"assertion":[{"value":"2022-11-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}