Inductive regression tree and genetic programming techniques for learning user Web search preferences

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Most Web search engines determine the relevancy of Web pages based on query terms, and a few content filtering applications allow consumers to block objectionable material. However, not many Web search engines and content filtering applications learn the user preferences over time. In this study, we proposed two machine-learning approaches that can be used to learn consumer preferences to identify documents that are most relevant to the consumer. We test the proposed machine learning approaches on a few simulated data sets. The results of our study illustrate that data mining approaches can be used to design intelligent adaptive agents that can select the relevant Web pages, given query terms, for the user.

Original languageEnglish (US)
Pages (from-to)223-245
Number of pages23
JournalJournal of Organizational Computing and Electronic Commerce
Volume16
Issue number3-4
StatePublished - Dec 1 2006

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Genetic programming
Search engines
World Wide Web
Learning systems
Websites
Data mining

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

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