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Inf. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use \u201cstatic\u201d document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two \u201cdynamic\u201d document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.<\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3639817","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T21:47:57Z","timestamp":1704750477000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Intent-Oriented Dynamic Interest Modeling for Personalized Web Search"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6210-7757","authenticated-orcid":false,"given":"Yutong","family":"Bai","sequence":"first","affiliation":[{"name":"School of Information, Renmin University of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3530-3787","authenticated-orcid":false,"given":"Yujia","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, China"}]},{"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, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education, China and Gaoling School of Artificial Intelligence, Renmin University of China, China"}]}],"member":"320","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of the International Conference on Web Search and Data Mining (WSDM\u201909, Barcelona, Spain, February 9-11, 2009)","author":"Agrawal Rakesh","year":"2009","unstructured":"Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009. 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