A Retriever Independent Framework for Relevance Feedback - Zutshi, Samar
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
Samar Zutshi is a lecturer in Business Management and Technology at Swinburne University of Technology. Before that he was at the Faculty of Information Technology at Monash University, from where he received his PhD. Samar is a member of the ACM, IEEE and IIBA.