Adaptive information retrieval system applied to digital libraries
WebMedia '06 Proceedings of the 12th Brazilian Symposium on Multimedia and the web
Custom ordering on digital library information retrieval
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
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The volume and variety of the Internet information is exponentially grows and therefore causes difficulties for a user to obtain information that accurately matches of the user interested. Several combination techniques are used to achieve the precise goal. This is due, firstly, to the fact that users often do not present queries to information retrieval systems that optimally represent the information they want, and secondly, the measure of a document's relevance ishighly subjective and variable between different users. This paper addresses this problem with an approach that relies on evolutionary user-modelling, in order to retrieve domain-specific information. The paper describes an adaptive information retrieval system that learns user needs from user-provided relevance feedback. The method combines qualitative feedback measures using fuzzy inference, and quantitative feedback using genetic algorithms (GA) fitness measures. In this paper, we utilised the multi-agent design approach for designing an information retrieval system (IRS). The system consists of following combination of complexprocesses: document indexing, learning strategic for relevant feedback and user modelling using genetic algorithm, filtering and ranking the retrieve documents based on the user model. This paper shows the design of the IRS consists of several agents that cooperate with each other and may perform in parallel to achieve the system goal.