The Use of Dynamic Contexts to Improve Casual Internet Searching

Leroy, Gondy and Lally, Ann M. and Chen, Hsinchun (2003) The Use of Dynamic Contexts to Improve Casual Internet Searching. ACM Transactions on Information Systems 21(3):pp. 229-253.

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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.

EPrint Type:	Journal Article (Paginated)
Keywords:	National Science Digital Library, NSDL, Artificial Intelligence Lab, AI Lab, Information retrieval, personalization, Internet, genetic algorithm, relevance feedback, automatic query expansion, implicit user feedback
Subjects:	Human Computer Interaction Information Seeking Behaviors World Wide Web
ID Code:	431
Deposited On:	16 August 2004
Alternative Locations:	http://ai.bpa.arizona.edu/go/papers.html
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