User modelling using evolutionary interactive reinforcement learning |
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| Authors: | H O Nyongesa S Maleki-dizaji |
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| Institution: | (1) Department of Computer Science, University of Botswana, Private Bag 0022, Gaborone, Botswana;(2) School of Computing and Management Sciences, Sheffield Hallam University, S1 1WB, UK |
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| Abstract: | As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining
information that accurately matches user information needs. A number of factors affect information retrieval effectiveness
(the accuracy of matching user information needs against the retrieved information). First, users often do not present search
queries in the form that optimally represents their information need. Second, the measure of a document’s relevance is often
highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats
and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval
effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational
intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative
(subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An
information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall. |
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| Keywords: | User information needs modelling Interactive evolutionary learning Information relevance Adaptive information retrieval |
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