Search (42 results, page 1 of 3)

  • × theme_ss:"Retrievalalgorithmen"
  1. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.07 
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    Date
    20. 3.2005 16:23:22
  2. Longshu, L.; Xia, Z.: On an aproximate fuzzy information retrieval agent (1998) 0.03 
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  3. Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015) 0.03 
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    Abstract
    Das von der DFG geförderte Projekt LibRank erforscht neue Rankingverfahren für bibliothekarische Informationssysteme, die aufbauend auf Erkenntnissen aus dem Bereich Websuche qualitätsinduzierende Faktoren wie z. B. Aktualität, Popularität und Verfügbarkeit von einzelnen Medien berücksichtigen. Die konzipierten Verfahren werden im Kontext eines in den Wirtschaftswissenschaften häufig genutzten Rechercheportals (EconBiz) entwickelt und in einem Testsystem systematisch evaluiert. Es werden Rankingfaktoren, die für den Bibliotheksbereich von besonderem Interesse sind, vorgestellt und exemplarisch Probleme und Herausforderungen aufgezeigt.
  4. Pan, M.; Huang, J.X.; He, T.; Mao, Z.; Ying, Z.; Tu, X.: ¬A simple kernel co-occurrence-based enhancement for pseudo-relevance feedback (2020) 0.02 
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  5. Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018) 0.02 
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    Abstract
    Discovery-Systeme bieten meist als Standardeinstellung eine Sortierung nach Relevanz an. Wie die Relevanz ermittelt wird, ist häufig intransparent. Dabei wären Kenntnisse darüber aus Nutzersicht ein wichtiger Faktor in der Informationskompetenz, während Bibliotheken sicherstellen sollten, dass das Ranking zum eigenen Bestand und Publikum passt. In diesem Aufsatz wird dargestellt, wie Discovery-Systeme Treffer auswählen und bewerten. Dazu gehören Indexierung, Prozessierung, Text-Matching und weitere Relevanzkriterien, z. B. Popularität oder Verfügbarkeit. Schließlich müssen alle betrachteten Kriterien zu einem zentralen Score zusammengefasst werden. Ein besonderer Fokus wird auf das Ranking von EBSCO Discovery Service, Primo und Summon gelegt.
  6. Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019) 0.02 
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    Abstract
    Dieser Beitrag beschreibt eine Studie zur Entwicklung und Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme. Dazu wurden mögliche Faktoren für das Relevanzranking ausgehend von den Verfahren in Websuchmaschinen identifiziert, auf den Bibliothekskontext übertragen und systematisch evaluiert. Mithilfe eines Testsystems, das auf dem ZBW-Informationsportal EconBiz und einer web-basierten Software zur Evaluierung von Suchsystemen aufsetzt, wurden verschiedene Relevanzfaktoren (z. B. Popularität in Verbindung mit Aktualität) getestet. Obwohl die getesteten Rankingverfahren auf einer theoretischen Ebene divers sind, konnten keine einheitlichen Verbesserungen gegenüber den Baseline-Rankings gemessen werden. Die Ergebnisse deuten darauf hin, dass eine Adaptierung des Rankings auf individuelle Nutzer bzw. Nutzungskontexte notwendig sein könnte, um eine höhere Performance zu erzielen.
  7. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  8. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.02 
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    Date
    30. 3.2001 13:32:22
  9. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.02 
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    Date
    25. 8.2005 17:42:22
  10. Li, H.; Wu, H.; Li, D.; Lin, S.; Su, Z.; Luo, X.: PSI: A probabilistic semantic interpretable framework for fine-grained image ranking (2018) 0.02 
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  11. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:44
  12. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02 
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    14. 6.2015 22:12:56
  13. Chen, Z.; Meng, X.; Fowler, R.H.; Zhu, B.: Real-time adaptive feature and document learning for Web search (2001) 0.02 
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  14. Chen, Z.; Fu, B.: On the complexity of Rocchio's similarity-based relevance feedback algorithm (2007) 0.02 
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  15. Li, M.; Li, H.; Zhou, Z.-H.: Semi-supervised document retrieval (2009) 0.02 
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  16. Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015) 0.02 
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  17. Ye, Z.; Huang, J.X.: ¬A learning to rank approach for quality-aware pseudo-relevance feedback (2016) 0.02 
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  18. Jiang, X.; Sun, X.; Yang, Z.; Zhuge, H.; Lapshinova-Koltunski, E.; Yao, J.: Exploiting heterogeneous scientific literature networks to combat ranking bias : evidence from the computational linguistics area (2016) 0.02 
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  19. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.02 
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  20. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01 
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    Date
    20. 1.2007 18:30:22

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