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  • × theme_ss:"Retrievalstudien"
  1. Mielke, B.: Wider einige gängige Ansichten zur juristischen Informationserschließung (2002) 0.01 
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  2. Breuer, T.; Tavakolpoursaleh, N.; Schaer, P.; Hienert, D.; Schaible, J.; Castro, L.J.: Online Information Retrieval Evaluation using the STELLA Framework (2022) 0.01 
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    Abstract
    Involving users in early phases of software development has become a common strategy as it enables developers to consider user needs from the beginning. Once a system is in production, new opportunities to observe, evaluate and learn from users emerge as more information becomes available. Gathering information from users to continuously evaluate their behavior is a common practice for commercial software, while the Cranfield paradigm remains the preferred option for Information Retrieval (IR) and recommendation systems in the academic world. Here we introduce the Infrastructures for Living Labs STELLA project which aims to create an evaluation infrastructure allowing experimental systems to run along production web-based academic search systems with real users. STELLA combines user interactions and log files analyses to enable large-scale A/B experiments for academic search.
  3. Petras, V.; Womser-Hacker, C.: Evaluation im Information Retrieval (2023) 0.01 
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    Abstract
    Das Ziel einer Evaluation ist die Überprüfung, ob bzw. in welchem Ausmaß ein Informationssystem die an das System gestellten Anforderungen erfüllt. Informationssysteme können aus verschiedenen Perspektiven evaluiert werden. Für eine ganzheitliche Evaluation (als Synonym wird auch Evaluierung benutzt), die unterschiedliche Qualitätsaspekte betrachtet (z. B. wie gut ein System relevante Dokumente rankt, wie schnell ein System die Suche durchführt, wie die Ergebnispräsentation gestaltet ist oder wie Suchende durch das System geführt werden) und die Erfüllung mehrerer Anforderungen überprüft, empfiehlt es sich, sowohl eine perspektivische als auch methodische Triangulation (d. h. der Einsatz von mehreren Ansätzen zur Qualitätsüberprüfung) vorzunehmen. Im Information Retrieval (IR) konzentriert sich die Evaluation auf die Qualitätseinschätzung der Suchfunktion eines Information-Retrieval-Systems (IRS), wobei oft zwischen systemzentrierter und nutzerzentrierter Evaluation unterschieden wird. Dieses Kapitel setzt den Fokus auf die systemzentrierte Evaluation, während andere Kapitel dieses Handbuchs andere Evaluationsansätze diskutieren (s. Kapitel C 4 Interaktives Information Retrieval, C 7 Cross-Language Information Retrieval und D 1 Information Behavior).
  4. Nelson, M.J.: ¬The effect of query characteristics on retrieval results in the TREC retrieval tests (1995) 0.00 
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    Source
    Connectedness: information, systems, people, organizations. Proceedings of CAIS/ACSI 95, the proceedings of the 23rd Annual Conference of the Canadian Association for Information Science. Ed. by Hope A. Olson and Denis B. Ward
  5. Greisdorf, H.; O'Connor, B.: Nodes of topicality modeling user notions of on topic documents (2003) 0.00 
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  6. Hemminger, B.M.; Saelim, B.; Sullivan, P.F.; Vision, T.J.: Comparison of full-text searching to metadata searching for genes in two biomedical literature cohorts (2007) 0.00 
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  7. Scherer, B.: Automatische Indexierung und ihre Anwendung im DFG-Projekt "Gemeinsames Portal für Bibliotheken, Archive und Museen (BAM)" (2003) 0.00 
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  8. Sarigil, E.; Sengor Altingovde, I.; Blanco, R.; Barla Cambazoglu, B.; Ozcan, R.; Ulusoy, Ö.: Characterizing, predicting, and handling web search queries that match very few or no results (2018) 0.00 
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  9. Vegt, A. van der; Zuccon, G.; Koopman, B.: Do better search engines really equate to better clinical decisions? : If not, why not? (2021) 0.00 
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  10. Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997) 0.00 
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    Date
    27. 2.1999 20:55:22
  11. Ng, K.B.; Loewenstern, D.; Basu, C.; Hirsh, H.; Kantor, P.B.: Data fusion of machine-learning methods for the TREC5 routing tak (and other work) (1997) 0.00 
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    Date
    27. 2.1999 20:59:22
  12. Saracevic, T.: On a method for studying the structure and nature of requests in information retrieval (1983) 0.00 
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    Pages
    S.22-25
  13. Naderi, H.; Rumpler, B.: PERCIRS: a system to combine personalized and collaborative information retrieval (2010) 0.00 
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  14. Rijsbergen, C.J. van: ¬A test for the separation of relevant and non-relevant documents in experimental retrieval collections (1973) 0.00 
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    Date
    19. 3.1996 11:22:12
  15. Sanderson, M.: ¬The Reuters test collection (1996) 0.00 
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  16. Lespinasse, K.: TREC: une conference pour l'evaluation des systemes de recherche d'information (1997) 0.00 
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    Date
    1. 8.1996 22:01:00
  17. ¬The Fifth Text Retrieval Conference (TREC-5) (1997) 0.00 
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    Abstract
    Proceedings of the 5th TREC-confrerence held in Gaithersburgh, Maryland, Nov 20-22, 1996. Aim of the conference was discussion on retrieval techniques for large test collections. Different research groups used different techniques, such as automated thesauri, term weighting, natural language techniques, relevance feedback and advanced pattern matching, for information retrieval from the same large database. This procedure makes it possible to compare the results. The proceedings include papers, tables of the system results, and brief system descriptions including timing and storage information
  18. Pemberton, J.K.; Ojala, M.; Garman, N.: Head to head : searching the Web versus traditional services (1998) 0.00 
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    Source
    Online. 22(1998) no.3, S.24-26,28
  19. Dresel, R.; Hörnig, D.; Kaluza, H.; Peter, A.; Roßmann, A.; Sieber, W.: Evaluation deutscher Web-Suchwerkzeuge : Ein vergleichender Retrievaltest (2001) 0.00 
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    Abstract
    Die deutschen Suchmaschinen, Abacho, Acoon, Fireball und Lycos sowie die Web-Kataloge Web.de und Yahoo! werden einem Qualitätstest nach relativem Recall, Precision und Availability unterzogen. Die Methoden der Retrievaltests werden vorgestellt. Im Durchschnitt werden bei einem Cut-Off-Wert von 25 ein Recall von rund 22%, eine Precision von knapp 19% und eine Verfügbarkeit von 24% erreicht
  20. Ellis, D.: Progress and problems in information retrieval (1996) 0.00 
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    Date
    26. 7.2002 20:22:46

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