{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T20:04:32Z","timestamp":1762027472647,"version":"build-2065373602"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p>The problem of finding an item-set of maximal aggregated utility that satisfies a set of constraints is at the cornerstone of many e-commerce applications. Its classical definition assumes that all the information needed to verify the constraints is explicitly given. In practice, however, the data available in e-commerce databases on the items is often partial. Hence, adequately answering constrained search queries requires the completion of this missing information. A common approach to complete missing data is to employ Machine Learning (ML) algorithms. However, ML is naturally error-prone. More accurate data can be obtained by asking the items' sellers to complete missing data. But as the number of items in the repository is huge, asking sellers about all items is prohibitively expensive. CONCIERGE, our presented system, assists the e-commerce platform in identifying a bounded-size set of items whose data should be manually completed, as these items are expected to contribute the most to the constrained search queries in question. We demonstrate the effectiveness of our system on real-world data and scenarios taken from a large e-commerce system by interacting with the VLDB'20 participants who act as both analysts and the sellers.<\/jats:p>","DOI":"10.14778\/3415478.3415495","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T18:46:35Z","timestamp":1600109195000},"page":"2865-2868","source":"Crossref","is-referenced-by-count":2,"title":["CONCIERGE"],"prefix":"10.14778","volume":"13","author":[{"given":"Ido","family":"Guy","sequence":"first","affiliation":[{"name":"eBay Research"}]},{"given":"Tova","family":"Milo","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}]},{"given":"Slava","family":"Novgorodov","sequence":"additional","affiliation":[{"name":"eBay Research"}]},{"given":"Brit","family":"Youngmann","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}]}],"member":"320","published-online":{"date-parts":[[2020,8]]},"reference":[{"volume-title":"https:\/\/bit.ly\/2QiaBHE","year":"2020","key":"e_1_2_1_1_1","unstructured":"Online appendix. https:\/\/bit.ly\/2QiaBHE, 2020."},{"key":"e_1_2_1_2_1","volume-title":"Stanford","author":"Benjelloun O.","year":"2005","unstructured":"O. Benjelloun, A. D. Sarma, A. Halevy, and J. Widom. Uldbs: Databases with uncertainty and lineage. Technical report, Stanford, 2005."},{"key":"e_1_2_1_3_1","volume-title":"Ranking with fairness constraints. arXiv preprint arXiv:1704.06840","author":"Celis L. E.","year":"2017","unstructured":"L. E. Celis, D. Straszak, and N. K. Vishnoi. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840, 2017."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-006-0004-3"},{"key":"e_1_2_1_5_1","volume-title":"ICML","author":"Ho C.-J.","year":"2013","unstructured":"C.-J. Ho, S. Jabbari, and J. W. Vaughan. Adaptive task assignment for crowdsourced classification. In ICML, 2013."},{"key":"e_1_2_1_6_1","volume-title":"KDD","author":"Kannan A.","year":"2011","unstructured":"A. Kannan, I. E. Givoni, R. Agrawal, and A. Fuxman. Matching unstructured product offers to structured product specifications. In KDD, 2011."},{"key":"e_1_2_1_7_1","volume-title":"EDBT","author":"Stoyanovich J.","year":"2018","unstructured":"J. Stoyanovich, K. Yang, and H. Jagadish. Online set selection with fairness and diversity constraints. In EDBT, 2018."},{"issue":"13","key":"e_1_2_1_8_1","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.14778\/2733004.2733024","article-title":"Chimera: Large-scale classification using machine learning, rules, and crowdsourcing","volume":"7","author":"Sun C.","year":"2014","unstructured":"C. Sun, N. Rampalli, F. Yang, and A. Doan. Chimera: Large-scale classification using machine learning, rules, and crowdsourcing. Proc. VLDB Endow., 7(13):1529--1540, 2014.","journal-title":"Proc. VLDB Endow."},{"issue":"5","key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"485","DOI":"10.14778\/2735479.2735482","article-title":"Hear the whole story: Towards the diversity of opinion in crowdsourcing markets","volume":"8","author":"Wu T.","year":"2015","unstructured":"T. Wu, L. Chen, P. Hui, C. J. Zhang, and W. Li. Hear the whole story: Towards the diversity of opinion in crowdsourcing markets. Proc. VLDB Endow., 8(5):485--496, 2015.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_10_1","volume-title":"SIGMOD","author":"Zheng Y.","year":"2015","unstructured":"Y. Zheng, J. Wang, G. Li, R. Cheng, and J. Feng. Qasca: A quality-aware task assignment system for crowdsourcing applications. In SIGMOD, 2015."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3415478.3415495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T02:28:09Z","timestamp":1758076089000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3415478.3415495"}},"subtitle":["improving constrained search results by data melioration"],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":10,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10.14778\/3415478.3415495"],"URL":"https:\/\/doi.org\/10.14778\/3415478.3415495","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2020,8]]}}}