{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:38:13Z","timestamp":1758271093604,"version":"3.41.0"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872370 and 61832017"],"award-info":[{"award-number":["61872370 and 61832017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Outstanding Young Scientist Program","award":["BJJWZYJH012019100020098"],"award-info":[{"award-number":["BJJWZYJH012019100020098"]}]},{"name":"Intelligent Social Governance Platform, Major Innovation & Planning Interdisciplinary Platform for the \u201cDouble-First Class\u201d Initiative, Renmin University of China"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>Recent studies show that historical behaviors (such as queries and their clicks) contained in a search session can benefit the ranking performance of subsequent queries in the session. Existing neural context-aware ranking models usually rank documents based on either latent representations of user search behaviors or the word-level interactions between the candidate document and each historical behavior in the search session. However, these two kinds of models both have their own drawbacks. Representation-based models neglect fine-grained information on word-level interactions, whereas interaction-based models suffer from the length restriction of session sequence because of the large cost of word-level interactions. To complement the limitations of these two kinds of models, we propose a unified context-aware document ranking model that takes full advantage of both representation and interaction. Specifically, instead of matching a candidate document with every single historical query in a session, we encode the session history into a latent representation and use this representation to enhance the current query and the candidate document. We then just match the enhanced query and candidate document with several matching components to capture the fine-grained information of word-level interactions. Rich experiments on two public query logs prove the effectiveness and efficiency of our model for leveraging representation and interaction.<\/jats:p>","DOI":"10.1145\/3529955","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T10:12:02Z","timestamp":1649931122000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Integrating Representation and Interaction for Context-Aware Document Ranking"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9812-0438","authenticated-orcid":false,"given":"Haonan","family":"Chen","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-948X","authenticated-orcid":false,"given":"Zhicheng","family":"Dou","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9502-478X","authenticated-orcid":false,"given":"Qiannan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2371-1329","authenticated-orcid":false,"given":"Xiaochen","family":"Zuo","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"6th International Conference on Learning Representations (ICLR\u201918), Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings","author":"Ahmad Wasi Uddin","year":"2018","unstructured":"Wasi Uddin Ahmad, Kai-Wei Chang, and Hongning Wang. 2018. 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