{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T20:16:12Z","timestamp":1760472972747,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030722395"},{"type":"electronic","value":"9783030722401"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":88,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neural LETOR approaches have become a competitive alternative to traditional ones like LambdaMART. However, neural architectures performance grew proportionally to their complexity and size. This can be an obstacle for their adoption in large-scale search systems where a model size impacts latency and update time. For this reason, we propose an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up\u00a0to 60% without affecting the system performance. This approach also allows to reduce a LETOR model complexity and, therefore, its training and inference time up\u00a0to 50%.<\/jats:p>","DOI":"10.1007\/978-3-030-72240-1_34","type":"book-chapter","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T14:49:01Z","timestamp":1617288541000},"page":"342-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Neural Feature Selection for Learning to Rank"],"prefix":"10.1007","author":[{"given":"Alberto","family":"Purpura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karolina","family":"Buchner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianmaria","family":"Silvello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gian Antonio","family":"Susto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"34_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Advances in Neural Information Processing Systems, pp. 9505\u20139515 (2018)"},{"key":"34_CR2","first-page":"135","volume":"2018","author":"Q Ai","year":"2018","unstructured":"Ai, Q., Bi, K., Guo, J., Croft, W.: Learning a deep listwise context model for ranking refinement. Proc. SIGIR 2018, 135\u2013144 (2018)","journal-title":"Proc. SIGIR"},{"key":"34_CR3","first-page":"1241","volume":"2019","author":"S Bruch","year":"2019","unstructured":"Bruch, S., Zoghi, M., Bendersky, M., Najork, M.: Revisiting approximate metric optimization in the age of deep neural networks. Proc. SIGIR 2019, 1241\u20131244 (2019)","journal-title":"Proc. SIGIR"},{"issue":"23\u2013581","key":"34_CR4","first-page":"81","volume":"11","author":"CJ Burges","year":"2010","unstructured":"Burges, C.J.: From ranknet to lambdarank to lambdamart: an overview. Learning 11(23\u2013581), 81 (2010)","journal-title":"Learning"},{"key":"34_CR5","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1145\/1277741.1277811","volume":"2007","author":"X Geng","year":"2007","unstructured":"Geng, X., Liu, T., Qin, T., Li, H.: Feature selection for ranking. Proc. SIGIR 2007, 407\u2013414 (2007)","journal-title":"Proc. SIGIR"},{"key":"34_CR6","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1145\/2970398.2970433","volume":"2016","author":"A Gigli","year":"2016","unstructured":"Gigli, A., Lucchese, C., Nardini, F., Perego, R.: Fast feature selection for learning to rank. Proc. ICTIR 2016, 167\u2013170 (2016)","journal-title":"Proc. ICTIR"},{"issue":"1","key":"34_CR7","first-page":"54","volume":"18","author":"JC Gower","year":"1969","unstructured":"Gower, J.C., Ross, G.: Minimum spanning trees and single linkage cluster analysis. J. Royal Stat. Soc. Ser. C (Appl. Stat.) 18(1), 54\u201364 (1969)","journal-title":"J. Royal Stat. Soc. Ser. C (Appl. Stat.)"},{"key":"34_CR8","unstructured":"Han, X., Lei, S.: Feature selection and model comparison on microsoft learning-to-rank data sets. arXiv preprint arXiv:1803.05127 (2018)"},{"key":"34_CR9","unstructured":"Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3146\u20133154 (2017)"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Liu, T.: Learning to rank for information retrieval. Springer Science & Business Media (2011)","DOI":"10.1007\/978-3-642-14267-3"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Manning, C., Sch\u00fctze, H., Raghavan, P.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)","DOI":"10.1017\/CBO9780511809071"},{"key":"34_CR12","unstructured":"Pobrotyn, P., Bartczak, T., Synowiec, M., Bia\u0142obrzeski, R., Bojar, J.: Context-aware learning to rank with self-attention. arXiv preprint arXiv:2005.10084 (2020)"},{"key":"34_CR13","unstructured":"Qin, T., Liu, T.: Introducing letor 4.0 datasets. arXiv preprint arXiv:1306.2597 (2013)"},{"issue":"4","key":"34_CR14","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/s10791-009-9123-y","volume":"13","author":"T Qin","year":"2010","unstructured":"Qin, T., Liu, T.Y., Xu, J., Li, H.: Letor: a benchmark collection for research on learning to rank for information retrieval. Inf. Retrieval 13(4), 346\u2013374 (2010)","journal-title":"Inf. Retrieval"},{"key":"34_CR15","doi-asserted-by":"publisher","first-page":"53988","DOI":"10.1109\/ACCESS.2019.2902640","volume":"7","author":"A Rahangdale","year":"2019","unstructured":"Rahangdale, A., Raut, S.: Deep neural network regularization for feature selection in learning-to-rank. IEEE Access 7, 53988\u201354006 (2019)","journal-title":"IEEE Access"},{"key":"34_CR16","unstructured":"Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 (2016)"},{"key":"34_CR17","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Tonon, A., Vandin, F.: Permutation strategies for mining significant sequential patterns. In: Proceedings of ICDM 2019, pp. 1330\u20131335. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00169"},{"key":"34_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Zhuang, H., Wang, X., Bendersky, M., Najork, M.: Feature transformation for neural ranking models. In: Proceedings of SIGIR 2020 (2020)","DOI":"10.1145\/3397271.3401333"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72240-1_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T13:32:37Z","timestamp":1698845557000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-72240-1_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030722395","9783030722401"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72240-1_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 March 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"43","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ecir2021.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"436","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}