{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T19:16:10Z","timestamp":1772738170506,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,2]]},"DOI":"10.1145\/3664190.3672507","type":"proceedings-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T12:39:41Z","timestamp":1722861581000},"page":"107-116","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Personalized Beyond-accuracy Calibration in Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1667-2779","authenticated-orcid":false,"given":"Mohammadmehdi","family":"Naghiaei","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8843-4652","authenticated-orcid":false,"given":"Mahdi","family":"Dehghan","sequence":"additional","affiliation":[{"name":"Shahid Beheshti University, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2779-4942","authenticated-orcid":false,"given":"Hossein A.","family":"Rahmani","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8372-1679","authenticated-orcid":false,"given":"Javad","family":"Azizi","sequence":"additional","affiliation":[{"name":"Google, Palo Alto, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9447-4172","authenticated-orcid":false,"given":"Mohammad","family":"Aliannejadi","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2024,8,5]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:1907.13158","author":"Abdollahpouri Himan","year":"2019","unstructured":"Himan Abdollahpouri and Robin Burke. 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:1907.13158 (2019)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109912"},{"key":"e_1_3_2_1_3_1","volume-title":"The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286","author":"Abdollahpouri Himan","year":"2019","unstructured":"Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 (2019)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531708"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-021-09294-8"},{"key":"e_1_3_2_1_6_1","volume-title":"Recommender systems handbook","author":"Castells Pablo","unstructured":"Pablo Castells, Neil Hurley, and Saul Vargas. 2022. Novelty and diversity in recommender systems. In Recommender systems handbook. Springer, 603--646."},{"key":"e_1_3_2_1_7_1","volume-title":"Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053","author":"Caton Simon","year":"2020","unstructured":"Simon Caton and Christian Haas. 2020. Fairness in machine learning: A survey. arXiv preprint arXiv:2010.04053 (2020)."},{"key":"e_1_3_2_1_8_1","volume-title":"Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240","author":"Chen Jiawei","year":"2020","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020. Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240 (2020)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v16i1.19275"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347058"},{"key":"e_1_3_2_1_11_1","volume-title":"Recommender systems handbook","author":"Ekstrand Michael D","unstructured":"Michael D Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2022. Fairness in recommender systems. In Recommender systems handbook. Springer, 679--707."},{"key":"e_1_3_2_1_12_1","volume-title":"Conference on fairness, accountability and transparency. PMLR, 172--186","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 Conference on fairness, accountability and transparency. PMLR, 172--186."},{"key":"e_1_3_2_1_13_1","volume-title":"Eneldo Loza Menc\u00eda, and Klaus Brinker","author":"F\u00fcrnkranz Johannes","year":"2008","unstructured":"Johannes F\u00fcrnkranz, Eyke H\u00fcllermeier, Eneldo Loza Menc\u00eda, and Klaus Brinker. 2008. Multilabel classification via calibrated label ranking. Machine learning, Vol. 73, 2 (2008), 133--153."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864761"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102719"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102459"},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the workshop on recommender systems evaluation: dimensions and design (Workshop programme of the 8th ACM conference on recommender systems). Citeseer.","author":"Kaminskas Marius","year":"2014","unstructured":"Marius Kaminskas and Derek Bridge. 2014. Measuring surprise in recommender systems. In Proceedings of the workshop on recommender systems evaluation: dimensions and design (Workshop programme of the 8th ACM conference on recommender systems). Citeseer."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2926720"},{"key":"e_1_3_2_1_20_1","unstructured":"Toshihiro Kamishima Shotaro Akaho Hideki Asoh and Jun Sakuma. 2014. Correcting Popularity Bias by Enhancing Recommendation Neutrality.. In RecSys Posters."},{"key":"e_1_3_2_1_21_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_22_1","volume-title":"Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. In International Workshop on Algorithmic Bias in Search and Recommendation. Springer, 82--90","author":"Klimashevskaia Anastasiia","year":"2022","unstructured":"Anastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Christoph Trattner, and Lars Skj\u00e6rven. 2022. Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. In International Workshop on Algorithmic Bias in Search and Recommendation. Springer, 82--90."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_1_24_1","volume-title":"A Next Basket Recommendation Reality Check. CoRR","author":"Li Ming","year":"2021","unstructured":"Ming Li, Sami Jullien, Mozhdeh Ariannezhad, and Maarten de Rijke. 2021. A Next Basket Recommendation Reality Check. CoRR, Vol. abs\/2109.14233 (2021). [arXiv]2109.14233"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449866"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557713"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531959"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3622874"},{"key":"e_1_3_2_1_30_1","volume-title":"Combating the filter bubble: Designing for serendipity in a university course recommendation system. arXiv preprint arXiv:1907.01591","author":"Pardos Zachary A","year":"2019","unstructured":"Zachary A Pardos and Weijie Jiang. 2019. Combating the filter bubble: Designing for serendipity in a university course recommendation system. arXiv preprint arXiv:1907.01591 (2019)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531718"},{"key":"e_1_3_2_1_32_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)."},{"key":"e_1_3_2_1_33_1","volume-title":"Analysis of Biases in Calibrated Recommendations. In International Workshop on Algorithmic Bias in Search and Recommendation. Springer, 91--103","author":"Rojas Carlos","year":"2022","unstructured":"Carlos Rojas, David Contreras, and Maria Salam\u00f3. 2022. Analysis of Biases in Calibrated Recommendations. In International Workshop on Algorithmic Bias in Search and Recommendation. Springer, 91--103."},{"key":"e_1_3_2_1_34_1","first-page":"1","article-title":"Cornac: A Comparative Framework for Multimodal Recommender Systems","volume":"21","author":"Salah Aghiles","year":"2020","unstructured":"Aghiles Salah, Quoc-Tuan Truong, and Hady W Lauw. 2020. Cornac: A Comparative Framework for Multimodal Recommender Systems. Journal of Machine Learning Research, Vol. 21, 95 (2020), 1--5.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44593-5_25"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240372"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240372"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2645710.2645743"},{"key":"e_1_3_2_1_39_1","volume-title":"User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms. In Fifteenth ACM Conference on Recommender Systems. 133--142","author":"Wang Ningxia","year":"2021","unstructured":"Ningxia Wang and Li Chen. 2021. User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms. In Fifteenth ACM Conference on Recommender Systems. 133--142."},{"key":"e_1_3_2_1_40_1","volume-title":"Providing Item-side Individual Fairness for Deep Recommender Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 117--127","author":"Wang Xiuling","year":"2022","unstructured":"Xiuling Wang and Wendy Hui Wang. 2022. Providing Item-side Individual Fairness for Deep Recommender Systems. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 117--127."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102608"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380202"},{"key":"e_1_3_2_1_43_1","volume-title":"ICML","volume":"1","author":"Zadrozny Bianca","year":"2001","unstructured":"Bianca Zadrozny and Charles Elkan. 2001. Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In ICML, Vol. 1. Citeseer, 609--616."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1561\/1500000066"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462875"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/1060745.1060754"}],"event":{"name":"ICTIR '24: The 2024 ACM SIGIR International Conference on the Theory of Information Retrieval","location":"Washington DC USA","acronym":"ICTIR '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664190.3672507","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664190.3672507","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T23:58:57Z","timestamp":1755907137000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664190.3672507"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":46,"alternative-id":["10.1145\/3664190.3672507","10.1145\/3664190"],"URL":"https:\/\/doi.org\/10.1145\/3664190.3672507","relation":{},"subject":[],"published":{"date-parts":[[2024,8,2]]},"assertion":[{"value":"2024-08-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}