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Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
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- 14. 6.2015 22:12:44
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Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
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Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
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- Date
- 13. 9.2014 14:45:22
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Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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Dreßler, H.: Fuzzy Information Retrieval (2008)
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- Abstract
- Nach einer Erläuterung der Grundlagen der Fuzzylogik wird das Prinzip der unscharfen Suche dargestellt und die Unterschiede zum herkömmlichen Information Retrieval beschrieben. Am Beispiel der Suche nach Steinen für ein Mauerwerk wird gezeigt, wie eine unscharfe Suche in der D&WFuzzydatenbank erfolgreich durchgeführt werden kann und zu eindeutigen Ergebnissen führt.
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Fuhr, N.: Modelle im Information Retrieval (2013)
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- Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
-
Chakrabarti, S.; Dom, B.; Kumar, S.R.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Kleinberg, J.M.; Gibson, D.: Neue Pfade durch den Internet-Dschungel : Die zweite Generation von Web-Suchmaschinen (1999)
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Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013)
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Elsweiler, D.; Kruschwitz, U.: Interaktives Information Retrieval (2023)
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Bauckhage, C.: Marginalizing over the PageRank damping factor (2014)
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Koopman, R.: ¬Ein OPAC mit Gewichtungsalgorithmen : Der PICA Micro OPC (1996)
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Hüther, H.: Selix im DFG-Projekt Kascade (1998)
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Weinstein, A.: Hochprozentig : Tipps and tricks für ein Top-Ranking (2002)
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Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019)
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Reimer, U.: Empfehlungssysteme (2023)
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- Abstract
- Mit der wachsenden Informationsflut steigen die Anforderungen an Informationssysteme, aus der Menge potenziell relevanter Information die in einem bestimmten Kontext relevanteste zu selektieren. Empfehlungssysteme spielen hier eine besondere Rolle, da sie personalisiert - d. h. kontextspezifisch und benutzerindividuell - relevante Information herausfiltern können. Definition: Ein Empfehlungssystem empfiehlt einem Benutzer bzw. einer Benutzerin in einem definierten Kontext aus einer gegebenen Menge von Empfehlungsobjekten eine Teilmenge als relevant. Empfehlungssysteme machen Benutzer auf Objekte aufmerksam, die sie möglicherweise nie gefunden hätten, weil sie nicht danach gesucht hätten oder sie in der schieren Menge an insgesamt relevanter Information untergegangen wären.
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Fuhr, N.: Zur Überwindung der Diskrepanz zwischen Retrievalforschung und -praxis (1990)
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Mandl, T.: Web- und Multimedia-Dokumente : Neuere Entwicklungen bei der Evaluierung von Information Retrieval Systemen (2003)
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Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015)
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Fuhr, N.: Theorie des Information Retrieval I : Modelle (2004)
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- Grundlagen der praktischen Information und Dokumentation. 5., völlig neu gefaßte Ausgabe. 2 Bde. Hrsg. von R. Kuhlen, Th. Seeger u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried. Bd.1: Handbuch zur Einführung in die Informationswissenschaft und -praxis
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Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003)
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