Artificial Intelligence Lab, Department of MIS, University of Arizona / Research has shown that most usersâ 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 userâ s keywords with a
Boolean â and,â negative expansion adds terms to the userâ s keywords with a Boolean â not.â 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/106376 |
| Date | 07 1900 |
| Creators | Leroy, Gondy, Lally, Ann M., Chen, Hsinchun |
| Publisher | ACM |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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