A Retriever Independent Framework for Relevance Feedback
Classificatory Analysis Based Relevance Feedback for Content-Based Retrieval
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This work proposes a novel, classificatory analysis based relevance feedback framework based on a user-centric model of information need that is independent of any particular retrieval paradigm. The model of the user need is based on the principle that a complete representation of the user need is contained in an exhaustive user classification of the collection. This model provides a conceptually appealing basis for relevance feedback; each successive iteration of relevance feedback can be treated as a classification that becomes a closer approximation of the user's information need. The syste...
This work proposes a novel, classificatory analysis based relevance feedback framework based on a user-centric model of information need that is independent of any particular retrieval paradigm. The model of the user need is based on the principle that a complete representation of the user need is contained in an exhaustive user classification of the collection. This model provides a conceptually appealing basis for relevance feedback; each successive iteration of relevance feedback can be treated as a classification that becomes a closer approximation of the user's information need. The system iteratively achieves a better understanding of the user's information need, gradually converging to a satisfactory set of results. The framework is based on Rough Set Theory, which is explicitly designed to deal with classificatory analysis incorporating uncertainty and approximation.
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