{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:20:34Z","timestamp":1783437634859,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"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":[[2024,7,10]]},"DOI":"10.1145\/3626772.3657900","type":"proceedings-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T12:40:05Z","timestamp":1720701605000},"page":"2401-2405","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Explainable Uncertainty Attribution for Sequential Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5373-8920","authenticated-orcid":false,"given":"Carles","family":"Balsells-Rodas","sequence":"first","affiliation":[{"name":"Imperial College London, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0940-4218","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Amazon.com Inc, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5033-8910","authenticated-orcid":false,"given":"Zhishen","family":"Huang","sequence":"additional","affiliation":[{"name":"Amazon.com Inc, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8012-1392","authenticated-orcid":false,"given":"Yan","family":"Gao","sequence":"additional","affiliation":[{"name":"Amazon.com Inc, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Probabilistic Approach for Recommendation Systems. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 629--634","author":"Abdalla Nada","year":"2022","unstructured":"Nada Abdalla and Damien Forthomme. 2022. Probabilistic Approach for Recommendation Systems. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 629--634."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu Mohammad Ghavamzadeh Paul Fieguth Xiaochun Cao Abbas Khosravi U Rajendra Acharya et al. 2021. A review of uncertainty quantification in deep learning: Techniques applications and challenges. Information fusion Vol. 76 (2021) 243--297.","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"e_1_3_2_1_3_1","first-page":"20089","article-title":"Laplace redux-effortless bayesian deep learning","volume":"34","author":"Daxberger Erik","year":"2021","unstructured":"Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, and Philipp Hennig. 2021. Laplace redux-effortless bayesian deep learning. Advances in Neural Information Processing Systems , Vol. 34 (2021), 20089--20103.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3618408.3618708"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020655"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482145"},{"key":"e_1_3_2_1_7_1","volume-title":"international conference on machine learning. PMLR, 1050--1059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. PMLR, 1050--1059."},{"key":"e_1_3_2_1_8_1","volume-title":"The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis)","author":"Maxwell Harper F","year":"2015","unstructured":"F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), Vol. 5, 4 (2015), 1--19."},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on machine learning. PMLR, 4950--4961","author":"Joo Taejong","year":"2020","unstructured":"Taejong Joo, Uijung Chung, and Min-Gwan Seo. 2020. Being bayesian about categorical probability. In International conference on machine learning. PMLR, 4950--4961."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_11_1","volume-title":"Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems","author":"Lakshminarayanan Balaji","year":"2017","unstructured":"Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems , Vol. 30 (2017)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371786"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_1_15_1","volume-title":"Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems","author":"Sensoy Murat","year":"2018","unstructured":"Murat Sensoy, Lance Kaplan, and Melih Kandemir. 2018. Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems , Vol. 31 (2018)."},{"key":"e_1_3_2_1_16_1","volume-title":"Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation. Transactions on Machine Learning Research","author":"Ulmer Dennis","year":"2023","unstructured":"Dennis Ulmer, Christian Hardmeier, and Jes Frellsen. 2023. Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation. Transactions on Machine Learning Research (2023)."},{"key":"e_1_3_2_1_17_1","volume-title":"2023 a. Rethinking missing data: Aleatoric uncertainty-aware recommendation","author":"Wang Chenxu","year":"2023","unstructured":"Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, and Xiangnan He. 2023 a. Rethinking missing data: Aleatoric uncertainty-aware recommendation. IEEE Transactions on Big Data (2023)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01159"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570469"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531963"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531963"},{"key":"e_1_3_2_1_22_1","volume-title":"Xu Chen, and Ji-Rong Wen.","author":"Xu Lanling","year":"2023","unstructured":"Lanling Xu, Zhen Tian, Gaowei Zhang, Junjie Zhang, Lei Wang, Bowen Zheng, Yifan Li, Jiakai Tang, Zeyu Zhang, Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Xu Chen, and Ji-Rong Wen. 2023. Towards a More User-Friendly and Easy-to-Use Benchmark Library for Recommender Systems. 2837--2847."},{"key":"e_1_3_2_1_23_1","volume-title":"Deep density networks and uncertainty in recommender systems. arXiv preprint arXiv:1711.02487","author":"Zeldes Yoel","year":"2017","unstructured":"Yoel Zeldes, Stavros Theodorakis, Efrat Solodnik, Aviv Rotman, Gil Chamiel, and Dan Friedman. 2017. Deep density networks and uncertainty in recommender systems. arXiv preprint arXiv:1711.02487 (2017)."}],"event":{"name":"SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Washington DC USA","acronym":"SIGIR 2024","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626772.3657900","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626772.3657900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:20:42Z","timestamp":1755840042000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626772.3657900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":23,"alternative-id":["10.1145\/3626772.3657900","10.1145\/3626772"],"URL":"https:\/\/doi.org\/10.1145\/3626772.3657900","relation":{},"subject":[],"published":{"date-parts":[[2024,7,10]]},"assertion":[{"value":"2024-07-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}