This paper presents a document representation improvement technique named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called support this algorithm improves document representations and generates higher quality indexes. At the same time, the algorithm is efficient and scalable, suited for retrieval systems managing large document collections. The results of the preliminary evaluation reveal that the RFA algorithm is able to reduce the index dimensionality while improving retrieval effectiveness.
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