Relevance Feedback Evaluation
Relevance feedback evaluation measures how much a retrieval system improves when it reformulates a query using user judgments on the first results. The technique that defined the field is Rocchio's vector-space feedback, in which documents the user marks relevant pull the query vector toward themselves and documents marked non-relevant push it away; Salton and Buckley's 1990 study systematized its evaluation and showed substantial effectiveness gains. The central methodological challenge is fairness: because the user has already seen and judged some documents, naively re-scoring the whole collection rewards the system for re-finding documents it was just told about. Residual-collection and frozen-rank evaluation solve this by measuring improvement only on documents the user has not yet seen.
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Vyanzo
- Salton, G., & Buckley, C. (1990). Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4), 288-297. DOI: 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H ↗
- Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. ISBN: 9780521865715
- Voorhees, E. M., & Harman, D. K. (Eds.). (2005). TREC: Experiment and Evaluation in Information Retrieval. MIT Press. ISBN: 9780262220736
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 23). Relevance Feedback Evaluation (Measuring Retrieval Gains from Query Reformulation). ScholarGate. https://scholargate.app/sw/library-information-science/relevance-feedback-evaluation
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- Cranfield Evaluation ParadigmLibrary Information Science↔ linganisha
- Query Expansion EvaluationLibrary Information Science↔ linganisha
- TREC Pooling and Relevance JudgmentsLibrary Information Science↔ linganisha
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