방법 비교

	Query Log Analysis	Known-Item Search Success
분야	Library Information Science	Library Information Science
계열	Process / pipeline	Process / pipeline
기원 연도	2000	1968
창시자	Bernard J. Jansen, Amanda Spink & Tefko Saracevic; Silverstein et al.	William S. Cooper (expected search length); IR evaluation tradition
유형	Transaction-log analysis pipeline for search behavior	Evaluation pipeline for single-target (known-item) retrieval
원전	Jansen, B. J., Spink, A., & Saracevic, T. (2000). Real life, real users, and real needs: a study and analysis of user queries on the web. Information Processing & Management, 36(2), 207-227.	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.
별칭	Transaction Log Analysis, Search Log Analysis, Web Query Log Analysis, Query Transaction Analysis	Known-Item Retrieval Evaluation, Target Document Search Evaluation, Reciprocal Rank Evaluation, Known-Item Finding Success
관련	3	3
요약	Query log analysis — also called transaction-log analysis — studies the records that search systems automatically keep of what users typed, what they clicked, and when. Rather than asking users what they do or testing systems in the laboratory, it observes millions of real searches as they actually happened. The landmark studies by Jansen, Spink, and Saracevic on the Excite engine and by Silverstein and colleagues on AltaVista revealed a consistent and surprising picture: real web queries are very short, rarely use advanced operators, and users almost never look past the first page of results. By cleaning logs, reconstructing sessions, and tabulating term, query, and session statistics, the method turns raw server records into a behavioral portrait of how people really search.	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.