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Incremental relevance feedback
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ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
On the use of negation in Boolean IR queries
Information Processing and Management: an International Journal
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A Four-Factor User Interaction Model for Content-Based Image Retrieval
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Relevance feedback using weight propagation compared with information-theoretic query expansion
ECIR'07 Proceedings of the 29th European conference on IR research
What can quantum theory bring to information retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
On identifying representative relevant documents
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Relevance feedback using weight propagation
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Traditional information retrieval (IR) systems only allow users access to documents that match their current query, and therefore, users can only give relevance feedback on matching documents (or those with a matching strength greater than a set threshold. This article shows that, in systems that allow access to nonmatching documents (e.g., hybrid hypertext and information retrieval systems), the strength of the effect of giving relevance feedback varies between matching and nonmatching documents. For positive feedback the results shown here are encouraging, as they can be justified by an intuitive view of the process. However, for negative feedback the results show behavior that cannot easily be justified and that varies greatly depending on the model of feedback used.