Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404455
Title: Evolutionary learning multi-agent based information retrieval systems
Author: Maleki-Dizaji, Saeedeh
ISNI:       0000 0001 3617 3892
Awarding Body: Sheffield Hallam University
Current Institution: Sheffield Hallam University
Date of Award: 2003
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.404455  DOI: Not available
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