Identifying inter-domain similarities through content-based analysis of hierarchical web-directories

Shlomo Berkovsky, Dan Goldwasser, Tsvi Kuflik, Francesco Ricci

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Providing accurate personalized information services to the users requires knowing their interests and needs, as defined by their User Models (UMs). Since the quality of the personalization depends on the richness of the UMs, services would benefit from enriching their UMs through importing and aggregating partial UMs built by other services from relatively similar domains. The obvious question is how to determine the similarity of domains? This paper proposes to compute inter-domain similarities by exploiting well-known Information Retrieval techniques for comparing textual contents of the Web-sites, classified under the domain nodes in Web-directories. Initial experiments validate feasibility of the proposed approach and raise open research questions.

Original languageEnglish
Title of host publicationECAI 2006
Subtitle of host publication17th European Conference on Artificial Intelligence August 29 - September 1, 2006, Riva del Garda, Italy
EditorsGerhard Brewka, Silvia Coradeschi, Anna Perini, Paolo Traverso
PublisherIOS Press BV
Number of pages2
ISBN (Print)9781586036423
StatePublished - 2006

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

ASJC Scopus subject areas

  • Artificial Intelligence


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