Improving document representations using relevance feedback

Abstract

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.