A1 Journal article (refereed)
Behavior-based personalization in web search (2017)


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


JYU authors or editors


Publication details

All authors or editors: Cai, Fei; Wang, Shuaiqiang; de Rijke, Maarten

Journal or series: Journal of the Association for Information Science and Technology

ISSN: 2330-1635

eISSN: 2330-1643

Publication year: 2017

Volume: 68

Issue number: 4

Pages range: 855-868

Publisher: Wiley

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1002/asi.23735

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71174


Abstract

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.


Keywords: information retrieval; information retrieval systems; Internet; search services; personifying; customisation; individualisation (education)


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Preliminary JUFO rating: 3


Last updated on 2021-16-07 at 10:41