পদ্ধতির তুলনা করুন

	Search Session Analysis	Known-Item Search Success
ক্ষেত্র	Library Information Science	Library Information Science
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2000	1968
প্রবর্তক	Bernard J. Jansen, Amanda Spink & Tefko Saracevic; web-search session research	William S. Cooper (expected search length); IR evaluation tradition
ধরন	Analysis pipeline for multi-query search episodes	Evaluation pipeline for single-target (known-item) retrieval
মৌলিক উৎস	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.
অপর নাম	Session Analysis, Search Episode Analysis, Multi-Query Session Analysis, Session-Based Search Evaluation	Known-Item Retrieval Evaluation, Target Document Search Evaluation, Reciprocal Rank Evaluation, Known-Item Finding Success
সম্পর্কিত	3	3
সারসংক্ষেপ	Search session analysis studies the whole search episode — the sequence of queries, reformulations, clicks, and pauses a user produces while pursuing a single information need — rather than scoring one query in isolation. Real searching is rarely one shot: users issue a query, scan results, refine their wording, follow links, and try again until they succeed or give up. Building on the transaction-log tradition of Jansen, Spink, and Saracevic and the large-scale web studies of Silverstein and colleagues, session analysis reconstructs these episodes from logs, classifies how queries evolve, measures the effort expended, models the transitions between actions, and assesses whether and how the session succeeded. It is the bridge between single-query laboratory evaluation and the messy, iterative reality of how people actually find information.	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.