{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T12:49:31Z","timestamp":1756385371101,"version":"3.41.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            Ambiguity in queries is a common problem in information retrieval. There are currently two solutions: search result personalization and diversification. The former aims to tailor results for different users based on their preferences, but the limitations are redundant results and incomplete capture of user intents. The goal of the latter is to return results that cover as many aspects related to the query as possible. It improves diversity yet loses personality and cannot return the exact results the user wants. Intuitively, such two solutions can complement each other and bring more satisfactory reranking results. In this article, we propose a novel framework, namely,\n            <jats:bold>PnD<\/jats:bold>\n            , to integrate personalization and diversification reasonably. We employ the degree of refinding to determine the weight of personalization dynamically. Moreover, to improve the diversity and relevance of reranked results simultaneously, we design a reset RNN structure (RRNN) with the \u201creset gate\u201d to measure the influence of the newly selected document on novelty. Besides, we devise a \u201csubtopic learning layer\u201d to learn the virtual subtopics, which can yield fine-grained representations of queries, documents, and user profiles. Experimental results illustrate that our model can significantly outperform existing search result personalization and diversification methods.\n          <\/jats:p>","DOI":"10.1145\/3631989","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T11:42:46Z","timestamp":1699530166000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Personalized and Diversified: Ranking Search Results in an Integrated Way"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3013-4555","authenticated-orcid":false,"given":"Shuting","family":"Wang","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, 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\/0000-0002-3946-9178","authenticated-orcid":false,"given":"Jiongnan","family":"Liu","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9502-478X","authenticated-orcid":false,"given":"Qiannan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, China and Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education, China and Beijing Key Laboratory of Big Data Management and Analysis Methods, China"}]}],"member":"320","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1498759.1498766"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2348283.2348312"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2348283.2348312"},{"key":"e_1_3_1_5_2","first-page":"601","article-title":"Latent Dirichlet allocation","volume":"3","author":"Blei David","year":"2001","unstructured":"David Blei, Andrew Ng, and Michael Jordan. 2001. 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