Selective approach to handling topic oriented tasks on the World Wide Web

Amit C. Awekar, Kang Jaewoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

We address the problem of handling topic oriented tasks on the World Wide Web. Our aim is to find most relevant and important pages for broad-topic queries while searching in a small set of candidate pages. We present a link analysis based algorithm SelHITS which is an improvement over Kleinberg's HITS algorithm. We introduce concept of virtual links to exploit latent information in the hyperlinked environment. Selective expansion of the root set and novel ranking strategy are the distinguishing features of our approach. Selective expansion method avoids topic drift and provides results consistent with only one interpretation of the query. Experimental evaluation and user feedback show that our algorithm indeed distills the most relevant and important pages for broad-topic queries. Trends in user feedback suggests that there exists a uniform notion of quality of search results within users.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Pages343-348
Number of pages6
DOIs
Publication statusPublished - 2007 Sep 25
Event1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 - Honolulu, HI, United States
Duration: 2007 Apr 12007 Apr 5

Publication series

NameProceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Other

Other1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
CountryUnited States
CityHonolulu, HI
Period07/4/107/4/5

    Fingerprint

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Software
  • Theoretical Computer Science

Cite this

Awekar, A. C., & Jaewoo, K. (2007). Selective approach to handling topic oriented tasks on the World Wide Web. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 (pp. 343-348). [4221318] (Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007). https://doi.org/10.1109/CIDM.2007.368894