Search Session Analysis
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.
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Vyanzo
- 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. DOI: 10.1016/S0306-4573(99)00056-4 ↗
- Silverstein, C., Marais, H., Henzinger, M., & Moricz, M. (1999). Analysis of a very large web search engine query log. ACM SIGIR Forum, 33(1), 6-12. DOI: 10.1145/331403.331405 ↗
- Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. ISBN: 9780521865715
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 23). Search Session Analysis (Modeling Multi-Query Search Episodes). ScholarGate. https://scholargate.app/sw/library-information-science/search-session-analysis
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Weka mbinu hii kando ya jamaa zake wa karibu na uzisome bega kwa bega — maktaba huweka vitabu mezani; uamuzi ni wako.
- Known-Item Search SuccessLibrary Information Science↔ linganisha
- Query Log AnalysisLibrary Information Science↔ linganisha
- Relevance Feedback EvaluationLibrary Information Science↔ linganisha
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