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Koopman, R.: ¬Ein OPAC mit Gewichtungsalgorithmen : Der PICA Micro OPC (1996)
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Archuby, C.G.: Interfaces se recuperacion para catalogos en linea con salidas ordenadas por probable relevancia (2000)
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- Footnote
- Übers. d. Titels: Interface for retrieval from online access catalogues with ranked results according to their relevance
-
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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Information retrieval : data structures and algorithms (1992)
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- Content
- An edited volume containing data structures and algorithms for information retrieval including a disk with examples written in C. for prgrammers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents. ------------------Enthält die Kapitel: FRAKES, W.B.: Introduction to information storage and retrieval systems; BAEZA-YATES, R.S.: Introduction to data structures and algorithms related to information retrieval; HARMAN, D. u.a.: Inverted files; FALOUTSOS, C.: Signature files; GONNET, G.H. u.a.: New indices for text: PAT trees and PAT arrays; FORD, D.A. u. S. CHRISTODOULAKIS: File organizations for optical disks; FOX, C.: Lexical analysis and stoplists; FRAKES, W.B.: Stemming algorithms; SRINIVASAN, P.: Thesaurus construction; BAEZA-YATES, R.A.: String searching algorithms; HARMAN, D.: Relevance feedback and other query modification techniques; WARTIK, S.: Boolean operators; WARTIK, S. u.a.: Hashing algorithms; HARMAN, D.: Ranking algorithms; FOX, E.: u.a.: Extended Boolean models; RASMUSSEN, E.: Clustering algorithms; HOLLAAR, L.: Special-purpose hardware for information retrieval; STANFILL, C.: Parallel information retrieval algorithms
-
Dang, E.K.F.; Luk, R.W.P.; Allan, J.; Ho, K.S.; Chung, K.F.L.; Lee, D.L.: ¬A new context-dependent term weight computed by boost and discount using relevance information (2010)
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- Abstract
- We studied the effectiveness of a new class of context-dependent term weights for information retrieval. Unlike the traditional term frequency-inverse document frequency (TF-IDF), the new weighting of a term t in a document d depends not only on the occurrence statistics of t alone but also on the terms found within a text window (or "document-context") centered on t. We introduce a Boost and Discount (B&D) procedure which utilizes partial relevance information to compute the context-dependent term weights of query terms according to a logistic regression model. We investigate the effectiveness of the new term weights compared with the context-independent BM25 weights in the setting of relevance feedback. We performed experiments with title queries of the TREC-6, -7, -8, and 2005 collections, comparing the residual Mean Average Precision (MAP) measures obtained using B&D term weights and those obtained by a baseline using BM25 weights. Given either 10 or 20 relevance judgments of the top retrieved documents, using the new term weights yields improvement over the baseline for all collections tested. The MAP obtained with the new weights has relative improvement over the baseline by 3.3 to 15.2%, with statistical significance at the 95% confidence level across all four collections.
-
Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001)
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-
Fuhr, N.: Zur Überwindung der Diskrepanz zwischen Retrievalforschung und -praxis (1990)
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Harman, D.; Fox, E.; Baeza-Yates, R.; Lee, W.: Inverted files (1992)
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Harman, D.: Relevance feedback and other query modification techniques (1992)
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Harman, D.: Ranking algorithms (1992)
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-
Wilbur, W.J.: ¬A retrieval system based on automatic relevance weighting of search terms (1992)
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- Source
- Proceedings of the 55th Annual Meeting of the American Society for Information Science, Pittsburgh, 26.-29.10.92. Ed.: D. Shaw
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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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Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018)
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Rada, R.; Barlow, J.; Potharst, J.; Zanstra, P.; Bijstra, D.: Document ranking using an enriched thesaurus (1991)
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