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Preference modeling for personalized retrieval based on browsing history analysis
Author(s) -
Zhang Xiaoyu
Publication year - 2013
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21922
Subject(s) - preference , computer science , relevance (law) , information retrieval , relevance feedback , user modeling , field (mathematics) , personalized search , data mining , user interface , artificial intelligence , search engine , image retrieval , operating system , mathematics , political science , pure mathematics , law , economics , image (mathematics) , microeconomics
Personalized retrieval aims at meeting the personalized information need of users, in which preference modeling is of great importance. The user's preference can be revealed via user specification and relevance feedback, both of which require extra effort from the user and are inevitably labor intensive. In this paper, we propose a novel preference modeling algorithm based on browsing history analysis for personalized retrieval. Based on the browsing log, we explore the user's interest in both fields and field values and accumulatively update the preference model. Given a query, the relevant retrieval results can spontaneously be ranked according to their corresponding preference score without any extra user interference. Advanced settings are subsequently discussed to further improve the algorithm for practical use. Experimental results demonstrate the advantages of the proposed algorithm over previous work. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.