A retriever independent framework for relevance feedback

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/77196

Title: A retriever independent framework for relevance feedback
Author(s): Zutshi, Samar
Abstract: 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.
Publication type: Book
Publication year: 2010
Keyword(s): Relevance feedback
Publisher: VDM Verlag
ISBN: 9783639207798, 3639207793
Publisher URL: http://www.vdm-publishing.com/