Search (309 results, page 16 of 16)

  • × theme_ss:"Retrievalalgorithmen"
  1. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.00 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.948-966
  2. Silva, R.M.; Gonçalves, M.A.; Veloso, A.: ¬A Two-stage active learning method for learning to rank (2014) 0.00 
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    Journal of the Association for Information Science and Technology. 65(2014) no.1, S.109-128
  3. Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015) 0.00 
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    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.40-57
  4. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.00 
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    Journal of the Association for Information Science and Technology. 69(2018) no.9, S.1109-1121
  5. Jiang, J.-D.; Jiang, J.-Y.; Cheng, P.-J.: Cocluster hypothesis and ranking consistency for relevance ranking in web search (2019) 0.00 
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    Journal of the Association for Information Science and Technology. 70(2019) no.6, S.535-546
  6. Purpura, A.; Silvello, G.; Susto, G.A.: Learning to rank from relevance judgments distributions (2022) 0.00 
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    Journal of the Association for Information Science and Technology. 73(2022) no.9, S.1236-1252
  7. Efthimiadis, E.N.: Interactive query expansion : a user-based evaluation in a relevance feedback environment (2000) 0.00 
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    Source
    Journal of the American Society for Information Science. 51(2000) no.11, S.989-1003
  8. Bar-Ilan, J.; Levene, M.; Mat-Hassan, M.: Methods for evaluating dynamic changes in search engine rankings : a case study (2006) 0.00 
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    Abstract
    Purpose - The objective of this paper is to characterize the changes in the rankings of the top ten results of major search engines over time and to compare the rankings between these engines. Design/methodology/approach - The papers compare rankings of the top-ten results of the search engines Google and AlltheWeb on ten identical queries over a period of three weeks. Only the top-ten results were considered, since users do not normally inspect more than the first results page returned by a search engine. The experiment was repeated twice, in October 2003 and in January 2004, in order to assess changes to the top-ten results of some of the queries during the three months interval. In order to assess the changes in the rankings, three measures were computed for each data collection point and each search engine. Findings - The findings in this paper show that the rankings of AlltheWeb were highly stable over each period, while the rankings of Google underwent constant yet minor changes, with occasional major ones. Changes over time can be explained by the dynamic nature of the web or by fluctuations in the search engines' indexes. The top-ten results of the two search engines had surprisingly low overlap. With such small overlap, the task of comparing the rankings of the two engines becomes extremely challenging. Originality/value - The paper shows that because of the abundance of information on the web, ranking search results is of extreme importance. The paper compares several measures for computing the similarity between rankings of search tools, and shows that none of the measures is fully satisfactory as a standalone measure. It also demonstrates the apparent differences in the ranking algorithms of two widely used search engines.
  9. Marcus, S.: Textvergleich mit mehreren Mustern (2005) 0.00 
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    Abstract
    Das Gebiet des Pattern-Matching besitzt in vielen wissenschaftlichen Bereichen eine hohe Relevanz. Aufgrund unterschiedlicher Einsatzgebiete sind auch Umsetzung und Anwendung des Pattern-Matching sehr verschieden. Die allen Anwendungen des Pattern-Matching inhärente Aufgabe besteht darin, in einer Vielzahl von Eingabedaten bestimmte Muster wieder zu erkennen. Dies ist auch der deutschen Bezeichnung Mustererkennung zu entnehmen. In der Medizin findet Pattern-Matching zum Beispiel bei der Untersuchung von Chromosomensträngen auf bestimmte Folgen von Chromosomen Verwendung. Auf dem Gebiet der Bildverarbeitung können mit Hilfe des Pattern-Matching ganze Bilder verglichen oder einzelne Bildpunkte betrachtet werden, die durch ein Muster identifizierbar sind. Ein weiteres Einsatzgebiet des Pattern-Matching ist das Information-Retrieval, bei dem in gespeicherten Daten nach relevanten Informationen gesucht wird. Die Relevanz der zu suchenden Daten wird auch hier anhand eines Musters, zum Beispiel einem bestimmten Schlagwort, beurteilt. Ein vergleichbares Verfahren findet auch im Internet Anwendung. Internet-Benutzer, die mittels einer Suchmaschine nach bedeutsamen Informationen suchen, erhalten diese durch den Einsatz eines Pattern-Matching-Automaten. Die in diesem Zusammenhang an den Pattern-Matching-Automaten gestellten Anforderungen variieren mit der Suchanfrage, die an eine Suchmaschine gestellt wird. Eine solche Suchanfrage kann im einfachsten Fall aus genau einem Schlüsselwort bestehen. Im komplexeren Fall enthält die Anfrage mehrere Schlüsselwörter. Dabei muss für eine erfolgreiche Suche eine Konkatenation der in der Anfrage enthaltenen Wörter erfolgen. Zu Beginn dieser Arbeit wird in Kapitel 2 eine umfassende Einführung in die Thematik des Textvergleichs gegeben, wobei die Definition einiger grundlegender Begriffe vorgenommen wird. Anschließend werden in Kapitel 3 Verfahren zum Textvergleich mit mehreren Mustern vorgestellt. Dabei wird zunächst ein einfaches Vorgehen erläutert, um einen Einsteig in das Thema des Textvergleichs mit mehreren Mustern zu erleichtern. Danach wird eine komplexe Methode des Textvergleichs vorgestellt und anhand von Beispielen verdeutlicht.

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