Folksonomy-based personalized search by hybrid user profiles in multiple levels
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AbstractRecently, some systems have allowed users to rate and annotate resources, e.g., MovieLens, and we consider that it provides a way to identify favorite and non-favorite tags of a user by integrating his or her rating and tags. In this paper, we review and elaborate on the limitations of the current research on user profiling for personalized search in collaborative tagging systems. We then propose a new multi-level user profiling model by integrating tags and ratings to achieve personalized search, which can reflect not only a user's likes but also a his or her dislikes. To the best of our knowledge, this is the,first effort to integrate ratings and tags to model multi-level user profiles for personalized search. (C) 2016 Elsevier B.V. All rights reserved.
All Author(s) ListDu Q, Xie HR, Cai Y, Leung HF, Li Q, Min HQ, Wang FL
Name of ConferenceIEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)
Start Date of Conference18/10/2014
End Date of Conference19/10/2014
Place of ConferenceWuhan
Journal nameNeurocomputing
Year2016
Month9
Day5
Volume Number204
PublisherELSEVIER SCIENCE BV
Pages142 - 152
ISSN0925-2312
eISSN1872-8286
LanguagesEnglish-United Kingdom
KeywordsFolksonomy; Personalized search; Social tagging; User profiling; Web 2.0
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-31-07 at 02:27