Interactive relevance feedback with graded relevance and sentence extraction: simulated user experiments

Authors:
Kalervo Järvelin
Affiliations:
University of Tampere, Tampere, Finland
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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Abstract

Research on relevance feedback (RFB) in information retrieval (IR) has given mixed results. Success in RFB seems to depend on the searcher's willingness to provide feedback and ability to identify relevant documents or query keys. The paper is based on simulating many user scenarios regarding the amount and quality of RFB. In addition, we experiment with query-biased sentence extraction for query reformulation. The baselines are initial no-feedback queries and queries based on pseudo-relevance feedback. The core question is: under which conditions would RFB based on sentence extraction be successful? The answer depends on user's behavior, implementation of feedback query formulation, and the evaluation methods. A small amount of feedback from a short browsing window seems to improve the final ranking the most. Longer browsing allows more feedback and better queries but also consumes the available relevant documents.