Sammenlign metoder

	Relevance Feedback Evaluation	TREC Pooling and Relevance Judgments
Fagområde	Library Information Science	Library Information Science
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1990	2005
Ophavsperson	Gerard Salton & Chris Buckley (building on J. J. Rocchio)	Ellen M. Voorhees & Donna K. Harman (NIST TREC)
Type	Evaluation pipeline for relevance-feedback query reformulation	Pooled relevance-assessment pipeline for large test collections
Oprindelig kilde	Salton, G., & Buckley, C. (1990). Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4), 288-297.	Voorhees, E. M., & Harman, D. K. (Eds.). (2005). TREC: Experiment and Evaluation in Information Retrieval. MIT Press. ISBN: 9780262220736
Aliasser	Rocchio Feedback Evaluation, Feedback Effectiveness Measurement, Residual Collection Evaluation, Relevance Feedback Assessment	Pooling Method, Depth Pooling, TREC Pooling, Pooled Relevance Assessment
Relaterede	3	3
Resumé	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.	Pooling is the technique that lets the Cranfield evaluation paradigm scale to collections of millions of documents, where judging every document for every topic is impossible. Developed and institutionalized at the US National Institute of Standards and Technology for the Text REtrieval Conference (TREC), pooling gathers the top-ranked documents returned by many participating systems for each topic, merges them into a single pool, has human assessors judge only that pool, and treats every unjudged document as non-relevant. The result is a reusable test collection — documents, topics, and pooled relevance judgments (qrels) — on which new systems can later be scored without further assessment. Pooling is what made large-scale, reproducible retrieval evaluation feasible.