{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:00:33Z","timestamp":1774983633108,"version":"3.50.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,2,10]]},"abstract":"<jats:p>\n                    Top-\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    queries provide a ranked answer using a score that can either be given explicitly or computed from tuple values. Recommender systems use scores, based on user feedback on items with which they interact, to answer top-\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    queries. Such scores pose the challenge of correctly ranking elements using scores that are more often than not, uncertain. In this work, we address top-\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    queries based on uncertain scores. We propose to explicitly model the inherent uncertainty in the provided data and to consider a distribution of scores instead of a single score. Rooted in works of database probabilistic ranking, we offer the use of probabilistic ranking as a tool of choice for generating recommendation in the presence of uncertainty. We argue that the ranking approach should be chosen in a manner that maximizes user satisfaction, extending state-of-the-art on quality aspect of top-\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    answers over uncertain data, their relationship to top-\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    semantics, and improve ranking with uncertain scores in recommender systems. Towards this end, we introduce RankDist, an algorithm for efficiently computing probability of item position in a ranked recommendation. We show that rank-based (rather than score-based) methods that are computed using RankDist, which were not applied in recommender systems before, offer a guaranteed optimality by expectation and empirical superiority when tested on common benchmarks.\n                  <\/jats:p>","DOI":"10.1145\/3709655","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T15:45:06Z","timestamp":1739288706000},"page":"1-26","source":"Crossref","is-referenced-by-count":2,"title":["A Rank-Based Approach to Recommender System's Top-K Queries with Uncertain Scores"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5271-5646","authenticated-orcid":false,"given":"Coral","family":"Scharf","sequence":"first","affiliation":[{"name":"Technion -- Israel Institute of Technology, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0943-0329","authenticated-orcid":false,"given":"Carmel","family":"Domshlak","sequence":"additional","affiliation":[{"name":"Technion -- Israel Institute of Technology, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7028-661X","authenticated-orcid":false,"given":"Avigdor","family":"Gal","sequence":"additional","affiliation":[{"name":"Technion -- Israel Institute of Technology, Haifa, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5260-2287","authenticated-orcid":false,"given":"Haggai","family":"Roitman","sequence":"additional","affiliation":[{"name":"Ben-Gurion University of the Negev, Beer-Sheva, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proceedings of the 32nd International Conference on Very Large Data Bases","author":"Benjelloun Omar","year":"2006","unstructured":"Omar Benjelloun, Anish Das Sarma, Alon Y. Halevy, and Jennifer Widom. 2006. ULDBs: Databases with Uncertainty and Lineage. In Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, September 12--15, 2006,, Umeshwar Dayal, Kyu-Young Whang, David B. Lomet, Gustavo Alonso, Guy M. Lohman, Martin L. Kersten, Sang Kyun Cha, and Young-Kuk Kim (Eds.). ACM, 953--964. http:\/\/dl.acm.org\/citation.cfm?id=1164209"},{"key":"e_1_2_2_2_1","volume-title":"Recommender systems survey. Knowledge-based systems","author":"Bobadilla Jes\u00fas","year":"2013","unstructured":"Jes\u00fas Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Guti\u00e9rrez. 2013. Recommender systems survey. Knowledge-based systems, Vol. 46 (2013), 109--132."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.75"},{"key":"e_1_2_2_4_1","volume-title":"Estimating and Evaluating the Uncertainty of Rating Predictions and Top-n Recommendations in Recommender Systems. 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Stability and Multigroup Fairness in Ranking with Uncertain Predictions. arXiv preprint arXiv:2402.09326 (2024)."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589325"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3583140.3583151nolinkurl10.14778\/3583140.3583151"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457305"},{"key":"e_1_2_2_10_1","volume-title":"Computing All Restricted Skyline Probabilities on Uncertain Datasets. arXiv preprint arXiv:2303.00259","author":"Gao Xiangyu","year":"2023","unstructured":"Xiangyu Gao, Jianzhong Li, and Dongjing Miao. 2023. 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Synthesis lectures on data management","author":"Suciu Dan","year":"2011","unstructured":"Dan Suciu, Dan Olteanu, Christopher R\u00e9, and Christoph Koch. 2011. Probabilistic databases. Synthesis lectures on data management, Vol. 3, 2 (2011), 1--180."},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11251"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570469"},{"key":"e_1_2_2_37_1","volume-title":"A survey of queries over uncertain data. Knowledge and information systems","author":"Wang Yijie","year":"2013","unstructured":"Yijie Wang, Xiaoyong Li, Xiaoling Li, and Yuan Wang. 2013. A survey of queries over uncertain data. Knowledge and information systems, Vol. 37, 3 (2013), 485--530."},{"key":"e_1_2_2_38_1","volume-title":"Efficient processing of top-k queries in uncertain databases with x-relations","author":"Yi Ke","year":"2008","unstructured":"Ke Yi, Feifei Li, George Kollios, and Divesh Srivastava. 2008. 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