Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic document indexing from relevance feedback data
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Information storage and retrieval
Information storage and retrieval
Searching the Web: the public and their queries
Journal of the American Society for Information Science and Technology
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
A unified maximum likelihood approach to document retrieval
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Naming functions for the vector space model
ECIR'07 Proceedings of the 29th European conference on IR research
Local-feature-based image retrieval with weighted relevance feedback
International Journal of Business Intelligence and Data Mining
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In this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.