Behavior-based personalization in web search
Cai, F., Wang, S., & de Rijke, M. (2017). Behavior-based personalization in web search. Journal of the Association for Information Science and Technology, 68(4), 855-868. https://doi.org/10.1002/asi.23735
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Personalized search approaches tailor search results to users' current interests, so as to help improve the likelihood of a user finding relevant documents for their query. Previous work on personalized search focuses on using the content of the user's query and of the documents clicked to model the user's preference. In this paper we focus on a different type of signal: We investigate the use of behavioral information for the purpose of search personalization. That is, we consider clicks and dwell time for reranking an initially retrieved list of documents. In particular, we (i) investigate the impact of distributions of users and queries on document reranking; (ii) estimate the relevance of a document for a query at 2 levels, at the query‐level and at the word‐level, to alleviate the problem of sparseness; and (iii) perform an experimental evaluation both for users seen during the training period and for users not seen during training. For the latter, we explore the use of information from similar users who have been seen during the training period. We use the dwell time on clicked documents to estimate a document's relevance to a query, and perform Bayesian probabilistic matrix factorization to generate a relevance distribution of a document over queries. Our experiments show that: (i) for personalized ranking, behavioral information helps to improve retrieval effectiveness; and (ii) given a query, merging information inferred from behavior of a particular user and from behaviors of other users with a user‐dependent adaptive weight outperforms any combination with a fixed weight. ...
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis research was partially supported by the InnovationFoundation of NUDT for Postgraduate under No. B130503,the Academy of Finland (268078), the Natural ScienceFoundation of China (71402083), Amsterdam Data Science,the Dutch national program COMM IT, Elsevier, the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nr 312827 (VOX-Pol),the ESF Research Network Program ELIAS, the RoyalDutch Academy of Sciences (KNAW) under the Elite Net-work Shifts project, the Microsoft Research Ph.D. program,the Netherlands eScience Center under project number027.012.105, the Netherlands Institute for Sound andVision, the Netherlands Organisation for ScientificResearch (NWO) under project nos. 727.011.005,612.001.116, HOR-11-10, 640.006.013, 612.066.930, CI-14-25, SH-322-15, 652.002.001, 612.001.551, the Yahoo!Faculty Research and Engagement Program, and Yandex. ...
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