Search (90 results, page 1 of 5)

  • × theme_ss:"Retrievalalgorithmen"
  1. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.02
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    Date
    13. 9.2014 14:45:22
    Source
    http://www.searchmetrics.com/media/documents/knowledge-base/searchmetrics-ranking-faktoren-studie-2014.pdf
  2. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.02
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    Abstract
    Traces the development of text searching and retrieval software designed to cope with the increasing demands made by the storage and handling of large amounts of data, recorded on high data storage media, from CD-ROM to multi gigabyte storage media and online information services, with particular reference to the need to cope with graphics as well as conventional ASCII text. Includes details of: Boolean searching, fuzzy searching and matching; relevance ranking; proximity searching and improved strategies for dealing with text searching in very large databases. Concludes that the best searching tools for CD-ROM publishers are those optimized for searching and retrieval on CD-ROM. CD-ROM drives have relatively lower random seek times than hard discs and so the software most appropriate to the medium is that which can effectively arrange the indexes and text on the CD-ROM to avoid continuous random access searching. Lists and reviews a selection of software packages designed to achieve the sort of results required for rapid CD-ROM searching
    Date
    12. 9.1996 13:56:22
  3. Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013) 0.02
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    Abstract
    User interest as a very dynamic information need is often ignored in most existing information retrieval systems. In this research, we present the results of experiments designed to evaluate the performance of a real-time interest model (RIM) that attempts to identify the dynamic and changing query level interests regarding social media outputs. Unlike most existing ranking methods, our ranking approach targets calculation of the probability that user interest in the content of the document is subject to very dynamic user interest change. We describe 2 formulations of the model (real-time interest vector space and real-time interest language model) stemming from classical relevance ranking methods and develop a novel methodology for evaluating the performance of RIM using Amazon Mechanical Turk to collect (interest-based) relevance judgments on a daily basis. Our results show that the model usually, although not always, performs better than baseline results obtained from commercial web search engines. We identify factors that affect RIM performance and outline plans for future research.
    Date
    28. 7.2013 12:59:19
  4. Qi, Q.; Hessen, D.J.; Heijden, P.G.M. van der: Improving information retrieval through correspondenceanalysis instead of latent semantic analysis (2023) 0.01
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    Date
    15. 9.2023 12:28:29
  5. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.01
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    Date
    6. 1.1997 18:30:28
    11. 9.2004 17:32:22
  6. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.01
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    Abstract
    The digital library system Daffodil is targeted at strategic support of users during the information search process. For searching, exploring and managing digital library objects it provides user-customisable information seeking patterns over a federation of heterogeneous digital libraries. In this paper evaluation results with respect to retrieval effectiveness, efficiency and user satisfaction are presented. The analysis focuses on strategic support for the scientific work-flow. Daffodil supports the whole work-flow, from data source selection over information seeking to the representation, organisation and reuse of information. By embedding high level search functionality into the scientific work-flow, the user experiences better strategic system support due to a more systematic work process. These ideas have been implemented in Daffodil followed by a qualitative evaluation. The evaluation has been conducted with 28 participants, ranging from information seeking novices to experts. The results are promising, as they support the chosen model.
    Date
    16.11.2008 16:22:48
  7. Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018) 0.01
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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.
  8. Sparck Jones, K.: ¬A statistical interpretation of term specifity and its application in retrieval (1972) 0.01
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    Source
    Journal of documentation. 28(1972), S.11-21
  9. Liddy, E.D.; Diamond, T.; McKenna, M.: DR-LINK in TIPSTER (2000) 0.01
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    Date
    16. 8.2002 12:28:52
  10. Iker, H.P.: Solution of Boolean equations through the use of term weights to the base two (1967) 0.01
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    Date
    14. 5.1999 11:03:28
  11. Lee, J.H.: Combining the evidence of different relevance feedback methods for information retrieval (1998) 0.01
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    Date
    11. 8.2001 17:28:42
  12. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  13. Fuhr, N.: Modelle im Information Retrieval (2013) 0.01
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    Date
    5. 4.2013 13:47:28
  14. Archuby, C.G.: Interfaces se recuperacion para catalogos en linea con salidas ordenadas por probable relevancia (2000) 0.01
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    Date
    29. 1.1996 18:23:13
    Source
    Ciencia da informacao. 29(2000) no.3, S.5-13
  15. Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003) 0.01
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    Source
    Journal of information science. 29(2003) no.2, S.87-96
  16. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01
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    Date
    30. 3.2001 13:32:22
  17. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01
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    Date
    25. 8.2005 17:42:22
  18. Hoenkamp, E.; Bruza, P.: How everyday language can and will boost effective information retrieval (2015) 0.01
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    Abstract
    Typing 2 or 3 keywords into a browser has become an easy and efficient way to find information. Yet, typing even short queries becomes tedious on ever shrinking (virtual) keyboards. Meanwhile, speech processing is maturing rapidly, facilitating everyday language input. Also, wearable technology can inform users proactively by listening in on their conversations or processing their social media interactions. Given these developments, everyday language may soon become the new input of choice. We present an information retrieval (IR) algorithm specifically designed to accept everyday language. It integrates two paradigms of information retrieval, previously studied in isolation; one directed mainly at the surface structure of language, the other primarily at the underlying meaning. The integration was achieved by a Markov machine that encodes meaning by its transition graph, and surface structure by the language it generates. A rigorous evaluation of the approach showed, first, that it can compete with the quality of existing language models, second, that it is more effective the more verbose the input, and third, as a consequence, that it is promising for an imminent transition from keyword input, where the onus is on the user to formulate concise queries, to a modality where users can express more freely, more informal, and more natural their need for information in everyday language.
  19. Srinivasan, P.: Query expansion and MEDLINE (1996) 0.01
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    Date
    30. 3.2001 13:52:28
  20. Harman, D.; Fox, E.; Baeza-Yates, R.; Lee, W.: Inverted files (1992) 0.01
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    Pages
    S.28-43

Authors

Languages

  • e 74
  • d 15
  • pt 1
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Types

  • a 86
  • m 2
  • el 1
  • r 1
  • x 1
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