{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T17:11:30Z","timestamp":1782321090183,"version":"3.54.5"},"reference-count":251,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"name":"financial support provided by the National Natural Science Foundation of China","award":["No. U23A20305 and No. 62302345"],"award-info":[{"award-number":["No. U23A20305 and No. 62302345"]}]},{"name":"Xiaomi Young Scholar Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    Search engines, Recommender systems, and Online advertising are playing fundamental roles in modern web and mobile applications. In these information systems, the most significant component is the ranking system, which selects a list of items likely to interest a user from billions of candidate items. At its core, Deep learning to Rank (DLTR) has become indispensable for building high-performance ranking models, driving significant gains in user engagement and business growth. In this article, firstly, we outline the key problems and challenges in industrial-scale ranking systems. Secondly, we provide a comprehensive review of deep learning models deployed across multiple stages of the industrial ranking pipeline, including matching, pre-ranking, fine-grained ranking, post-ranking, and relevance-ranking. Finally, we explore novel perspectives for future research, such as leveraging Large Language Models (LLMs). The papers discussed in this survey are listed in\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/guyulongcs\/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising\">https:\/\/github.com\/guyulongcs\/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3797895","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T15:00:31Z","timestamp":1771254031000},"page":"1-52","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising: An Overview and New Perspectives"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7288-6910","authenticated-orcid":false,"given":"Yulong","family":"Gu","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6755-871X","authenticated-orcid":false,"given":"Lixin","family":"Zou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-6374","authenticated-orcid":false,"given":"Chenliang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"2952","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Abdool Mustafa","year":"2020","unstructured":"Mustafa Abdool, Malay Haldar, Prashant Ramanathan, Tyler Sax, Lanbo Zhang, Aamir Manaswala, Lynn Yang, Bradley Turnbull, Qing Zhang, and Thomas Legrand. 2020. 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