Web user clustering from access log using belief function

Yunjuan Xie, Vir V. Phoha

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

56 Scopus citations

Abstract

In this work, we present a novel approach to clustering Web site users into different groups and generating common user profiles. These profiles can be used to make recommendations, personalize Web sites, and for other uses such as targeting users for advertising. By using the concept of mass distribution in Dempster-Shafer's theory, the belief function similarity measure in our algorithm adds to the clustering task the ability to capture the uncertainty among Web user's navigation behavior. Our algorithm is relatively simple to use and gives comparable results to other approaches reported in the literature of web mining.

Original languageEnglish (US)
Title of host publicationProceedings of the First International Conference on Knowledge Capture
Pages202-208
Number of pages7
StatePublished - Dec 1 2001
Externally publishedYes
EventProceedings of the First International Conference on Knowledge Capture - Victoria, BC, Canada
Duration: Oct 21 2001Oct 23 2001

Publication series

NameProceedings of the First International Conference on Knowledge Capture

Other

OtherProceedings of the First International Conference on Knowledge Capture
CountryCanada
CityVictoria, BC
Period10/21/0110/23/01

Keywords

  • Access log
  • Clustering
  • Common user profile
  • Dempster-Shafer
  • Personalization
  • Web mining

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Xie, Y., & Phoha, V. V. (2001). Web user clustering from access log using belief function. In Proceedings of the First International Conference on Knowledge Capture (pp. 202-208). (Proceedings of the First International Conference on Knowledge Capture).