Authors:
Frederico Durao
1
;
Ricardo Lage
1
;
Peter Dolog
1
and
Nilay Coşkun
2
Affiliations:
1
IWIS — Intelligent Web and Information Systems and Aalborg University, Denmark
;
2
Istanbul Technical University, Turkey
Keyword(s):
Personalized, Search, Tagging, User learning, User preferences.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Ontologies and the Semantic Web
;
Personalized Web Sites and Services
;
Searching and Browsing
;
User Modeling
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Personalization
Abstract:
Coping with ambiguous queries has long been an important part in the research of Web Information Systems and Retrieval, but still remains to be a challenging task. Personalized search has recently got significant attention to address this challenge in the web search community, based on the premise that a user’s general preference may help the search engine disambiguate the true intention of a query. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. In this paper, we study how a search engine can learn a user’s preference automatically based on a user’s tagging activity and how it can use
the user preference to personalize search results. Our experiments show that users’ preferences can be learned from a multi-factor tagging data and personalized search based on user preference yields significant precision improvements over the existing ranking mechanisms in the literature.