Comparer des méthodes

	Query Expansion Evaluation	Relevance Feedback Evaluation
Domaine	Library Information Science	Library Information Science
Famille	Process / pipeline	Process / pipeline
Année d'origine	2008	1990
Auteur d'origine	Information retrieval evaluation tradition (Salton; Manning, Raghavan & Schütze)	Gerard Salton & Chris Buckley (building on J. J. Rocchio)
Type	Evaluation pipeline for query-expansion effectiveness	Evaluation pipeline for relevance-feedback query reformulation
Source fondatrice	Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. ISBN: 9780521865715	Salton, G., & Buckley, C. (1990). Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4), 288-297.
Alias	Query Reformulation Evaluation, Term Expansion Assessment, Expansion Effectiveness Measurement, Automatic Query Expansion Evaluation	Rocchio Feedback Evaluation, Feedback Effectiveness Measurement, Residual Collection Evaluation, Relevance Feedback Assessment
Apparentées	3	3
Résumé	Query expansion evaluation measures whether adding terms to a user's query — drawn from a thesaurus, from corpus co-occurrence statistics, or from relevance or pseudo-relevance feedback — actually improves retrieval. Expansion attacks the vocabulary-mismatch problem, where relevant documents use words the searcher did not, and it tends to raise recall by bringing in synonymous and related terms. But it can also lower precision by introducing ambiguity, and it can help some queries while badly hurting others. Sound evaluation therefore reports not just the average effectiveness change but the recall-precision trade-off and a robustness analysis of how many individual queries were helped versus harmed.	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.