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  • × theme_ss:"Retrievalalgorithmen"
  1. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.13
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    Date
    30. 3.2001 13:32:22
  2. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.07
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    Abstract
    Dieses Whitepaper beschäftigt sich mit der Definition und Bewertung von Faktoren, die eine hohe Rangkorrelation-Koeffizienz mit organischen Suchergebnissen aufweisen und dient dem Zweck der tieferen Analyse von Suchmaschinen-Algorithmen. Die Datenerhebung samt Auswertung bezieht sich auf Ranking-Faktoren für Google-Deutschland im Jahr 2014. Zusätzlich wurden die Korrelationen und Faktoren unter anderem anhand von Durchschnitts- und Medianwerten sowie Entwicklungstendenzen zu den Vorjahren hinsichtlich ihrer Relevanz für vordere Suchergebnis-Positionen interpretiert.
    Date
    13. 9.2014 14:45:22
  3. Van der Veer Martens, B.; Fleet, C. van: Opening the black box of "relevance work" : a domain analysis (2012) 0.07
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  4. Rijsbergen, C.J. van: ¬A fast hierarchic clustering algorithm (1970) 0.06
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  5. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.05
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    Date
    22. 3.2003 19:27:36
  6. Qi, Q.; Hessen, D.J.; Heijden, P.G.M. van der: Improving information retrieval through correspondenceanalysis instead of latent semantic analysis (2023) 0.05
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  7. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.04
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    Date
    22. 8.2014 17:05:18
  8. Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013) 0.04
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    Abstract
    Das Relevanz-Ranking im Katalog der Universitätsbibliothek Heidelberg HEIDI, bereits 2009 in einem Beitrag in dieser Zeitschrift beschrieben, wurde in den letzten Jahren durch neue Entwicklungen und Methoden stark verbessert. Der Aufsatz beschreibt die Realisierung der bisherigen Rankingmaßnahmen unter der neu eingesetzten Suchmaschinenplattform SOLR. Weiter werden verschiedene neue Möglichkeiten für Rankinganpassungen unter SOLR sowie deren Einsatz im HEIDI-Katalog dargestellt.
  9. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.03
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    Date
    22. 3.2013 19:34:49
  10. Sembok, T.M.T.; Rijsbergen, C.J. van: IMAGING: a relevant feedback retrieval with nearest neighbour clusters (1994) 0.03
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  11. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.03
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    Abstract
    Der Beitrag enthält eine Darstellung zur Frage der Konzeption von Rankingalgorithmen auf Grundlage gewichteter Indexierung mittels statistischer Verfahren.
    Date
    14. 6.2015 22:12:56
  12. ¬An introduction to information retrieval (o.J.) 0.02
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    Abstract
    In the beginning IR was dominated by Boolean retrieval, described in the next section. This could be called the antediluvian period, or generation zero. The first generation of IR research dates from the early sixties, and was dominated by model building, experimentation, and heuristics. The big names were Gerry Salton and Karen Sparck Jones. The second period, which began in the mid-seventies, saw a big shift towards mathematics, and a rise of the IR model based upon probability theory - probabilistic IR. The big name here was, and continues to be, Stephen Robertson. More recently Keith van Rijsbergen has led a group that has developed underlying logical models of IR, but interesting as this new work is, it has not as yet led to results that offer improvements for the IR system builder. Xapian is firmly placed as a system that implements, or tries to implement, the probabilistic IR model. (We say 'tries' because sometimes implementation efficiency and theoretical complexity demand certain short-cuts.)
  13. Ruthven, T.; Lalmas, M.; Rijsbergen, K.van: Incorporating user research behavior into relevance feedback (2003) 0.02
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    Abstract
    Ruthven, Mounia, and van Rijsbergen rank and select terms for query expansion using information gathered on searcher evaluation behavior. Using the TREC Financial Times and Los Angeles Times collections and search topics from TREC-6 placed in simulated work situations, six student subjects each preformed three searches on an experimental system and three on a control system with instructions to search by natural language expression in any way they found comfortable. Searching was analyzed for behavior differences between experimental and control situations, and for effectiveness and perceptions. In three experiments paired t-tests were the analysis tool with controls being a no relevance feedback system, a standard ranking for automatic expansion system, and a standard ranking for interactive expansion while the experimental systems based ranking upon user information on temporal relevance and partial relevance. Two further experiments compare using user behavior (number assessed relevant and similarity of relevant documents) to choose a query expansion technique against a non-selective technique and finally the effect of providing the user with knowledge of the process. When partial relevance data and time of assessment data are incorporated in term ranking more relevant documents were recovered in fewer iterations, however retrieval effectiveness overall was not improved. The subjects, none-the-less, rated the suggested terms as more useful and used them more heavily. Explanations of what the feedback techniques were doing led to higher use of the techniques.
  14. Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023) 0.02
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  15. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.02
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    Abstract
    Im Rahmen des klassischen Information Retrieval wurden verschiedene Verfahren für das Ranking sowie die Suche in einer homogenen strukturlosen Dokumentenmenge entwickelt. Die Erfolge der Suchmaschine Google haben gezeigt dass die Suche in einer zwar inhomogenen aber zusammenhängenden Dokumentenmenge wie dem Internet unter Berücksichtigung der Dokumentenverbindungen (Links) sehr effektiv sein kann. Unter den von der Suchmaschine Google realisierten Konzepten ist ein Verfahren zum Ranking von Suchergebnissen (PageRank), das in diesem Artikel kurz erklärt wird. Darüber hinaus wird auf die Konzepte eines Systems namens CiteSeer eingegangen, welches automatisch bibliographische Angaben indexiert (engl. Autonomous Citation Indexing, ACI). Letzteres erzeugt aus einer Menge von nicht vernetzten wissenschaftlichen Dokumenten eine zusammenhängende Dokumentenmenge und ermöglicht den Einsatz von Banking-Verfahren, die auf den von Google genutzten Verfahren basieren.
    Date
    20. 3.2005 16:23:22
  16. Schamber, L.; Bateman, J.: Relevance criteria uses and importance : progress in development of a measurement scale (1999) 0.02
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    Source
    Knowledge: creation, organization and use. Proceedings of the 62nd Annual Meeting of the American Society for Information Science, 31.10.-4.11.1999. Ed.: L. Woods
  17. Sakai, T.: On the reliability of information retrieval metrics based on graded relevance (2007) 0.02
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    Abstract
    This paper compares 14 information retrieval metrics based on graded relevance, together with 10 traditional metrics based on binary relevance, in terms of stability, sensitivity and resemblance of system rankings. More specifically, we compare these metrics using the Buckley/Voorhees stability method, the Voorhees/Buckley swap method and Kendall's rank correlation, with three data sets comprising test collections and submitted runs from NTCIR. Our experiments show that (Average) Normalised Discounted Cumulative Gain at document cut-off l are the best among the rank-based graded-relevance metrics, provided that l is large. On the other hand, if one requires a recall-based graded-relevance metric that is highly correlated with Average Precision, then Q-measure is the best choice. Moreover, these best graded-relevance metrics are at least as stable and sensitive as Average Precision, and are fairly robust to the choice of gain values.
  18. Zhao, L.; Wu, L.; Huang, X.: Using query expansion in graph-based approach for query-focused multi-document summarization (2009) 0.02
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  19. Longshu, L.; Xia, Z.: On an aproximate fuzzy information retrieval agent (1998) 0.02
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  20. Wartik, S.; Fox, E.; Heath, L.; Chen, Q.-F.: Hashing algorithms (1992) 0.02
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