Linganisha mbinu

	Known-Item Search Success	Cranfield Evaluation Paradigm
Nyanja	Library Information Science	Library Information Science
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1968	1967
Mwanzilishi	William S. Cooper (expected search length); IR evaluation tradition	Cyril W. Cleverdon
Aina	Evaluation pipeline for single-target (known-item) retrieval	Test-collection evaluation pipeline for retrieval effectiveness
Chanzo asilia	Cooper, W. S. (1968). Expected search length: A single measure of retrieval effectiveness based on the weak ordering action of retrieval systems. American Documentation, 19(1), 30-41.	Cleverdon, C. W. (1967). The Cranfield tests on index language devices. Aslib Proceedings, 19(6), 173-194.
Majina mbadala	Known-Item Retrieval Evaluation, Target Document Search Evaluation, Reciprocal Rank Evaluation, Known-Item Finding Success	Cranfield Methodology, Test Collection Evaluation, Cranfield Tests, Laboratory IR Evaluation
Zinazohusiana	3	3
Muhtasari	Known-item search is the case where the user is looking for one specific document they already know exists — a particular paper, book, web page, or record — rather than exploring a topic. Evaluation is correspondingly specialized: with exactly one correct answer per query, the question is simply how high the system ranks that single target. The natural measures are reciprocal rank (and its mean, MRR), success-at-k, and Cooper's expected search length, which counts how many wrong documents the user must wade through before reaching the right one. These metrics, averaged over many known-item topics, give a clean, interpretable picture of how well a system supports re-finding a specific document.	The Cranfield evaluation paradigm is the foundational experimental design for measuring how well an information retrieval system finds relevant documents. Devised by Cyril Cleverdon at the College of Aeronautics in Cranfield during the 1960s, it fixes three ingredients — a document collection, a set of search requests, and human relevance judgments linking requests to documents — and then holds them constant so that competing indexing methods or retrieval algorithms can be compared on recall and precision under controlled, repeatable conditions. By abstracting evaluation away from any single live user and turning it into a reusable laboratory experiment, Cranfield made retrieval effectiveness a measurable quantity and supplied the template that every later large-scale campaign, including TREC, has built upon.