The use of dynamic contexts to improve casual Internet searching

Gondy Leroy, Ann M. Lally, Hsinchun Chen

Research output: Contribution to journalReview article

24 Scopus citations

Abstract

Research has shown that most users' online information searches are suboptimal. Query optimization based on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback based on the surrounding links and text in a search engine result set to expand user queries with a variable number of keywords in two manners. Positive expansion adds terms to a user's keywords with a Boolean "and," negative expansion adds terms to the user's keywords with a Boolean "not." Each algorithm was examined for three user groups, high, middle, and low achievers, who were classified according to their overall performance. The interactions of users with different levels of expertise with different expansion types or algorithms were evaluated. The genetic algorithm with negative expansion tripled recall and doubled precision for low achievers, but high achievers displayed an opposed trend and seemed to be hindered in this condition. The effect of other conditions was less substantial.

Original languageEnglish (US)
Pages (from-to)229-253
Number of pages25
JournalACM Transactions on Information Systems
Volume21
Issue number3
DOIs
StatePublished - Jul 1 2003

Keywords

  • Automatic query expansion
  • Genetic algorithm
  • Implicit user feedback
  • Information retrieval
  • Internet
  • Personalization
  • Relevance feedback

ASJC Scopus subject areas

  • Information Systems
  • Business, Management and Accounting(all)
  • Computer Science Applications

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