Evolutionary learning multi-agent based information retrieval systems

MALEKI-DIZAJI, Saeedeh (2003). Evolutionary learning multi-agent based information retrieval systems. Doctoral, Sheffield Hallam University. [Thesis]

Documents
6856:12735
[thumbnail of Ethos]
Archive (ZIP) (Ethos)
Maleki_Dizaji_404455.zip - Accepted Version
Available under License All rights reserved.

Download (12MB)
6856:478938
[thumbnail of Version of Record]
Preview
PDF (Version of Record)
10697309.pdf - Accepted Version
Available under License All rights reserved.

Download (15MB) | Preview
Abstract
The volume and variety of information available on the Internet has experienced exponential growth, presenting a difficulty to users wishing to obtain information that accurately matches their interests. A number of factors affect the accuracy of matching user interests and the retrieved documents. First, is the fact that users often do not present queries to information retrieval systems in the form that optimally represents the information they want. Secondly, the measure of a document's relevance is highly subjective and variable between different users. This thesis addresses this problem with an adaptive approach that relies on evolutionary user-modelling. The proposed information retrieval system learns user needs from user-provided relevance feedback. The method combines a qualitative feedback measure obtained using fuzzy inference, and quantitative feedback based on evolutionary algorithms (Genetic Algorithms) fitness measures. Furthermore, the retrieval system's design approach is based on a multi-agent design approach, in order to handle the complexities of the information retrieval system including: document indexing, relevance feedback, user modelling, filtering and ranking the retrieve documents. The major contribution of this research are the combination of genetic algorithms and fuzzy relevance feedback for modelling adaptive behaviour, which is compared against conventional relevance feedback. Novel Genetic Algorithms operators are proposed within the context of textual; the encoding and vector space model for document representation is generalised within the same context.
More Information
Statistics

Downloads

Downloads per month over past year

View more statistics

Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Actions (login required)

View Item View Item