Search (13 results, page 1 of 1)

  • × theme_ss:"Retrievalalgorithmen"
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18
  2. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
  3. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.01 
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    Date
    11. 6.2012 14:22:34
  4. Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.01 
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    Date
    22. 3.2013 19:34:49
  5. Bar-Ilan, J.; Levene, M.: ¬The hw-rank : an h-index variant for ranking web pages (2015) 0.01 
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    Content
    Vgl.: doi:10.1007/s11192-014-1477-2
  6. Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018) 0.01 
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    Source
    Perspektive Bibliothek. 7(2018) H.2, S.2-23
  7. Oberhauser, O.: Relevance Ranking in den Online-Katalogen der "nächsten Generation" (2010) 0.01 
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    Source
    Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 63(2010) H.1/2, S.25-37
  8. Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.01 
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    Abstract
    In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.
  9. Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013) 0.01 
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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.
  10. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 0.01 
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    Source
    Information processing and management. 50(2014) no.2, S.416-425
  11. Hoenkamp, E.; Bruza, P.: How everyday language can and will boost effective information retrieval (2015) 0.00 
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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.
  12. Hubert, G.; Pitarch, Y.; Pinel-Sauvagnat, K.; Tournier, R.; Laporte, L.: TournaRank : when retrieval becomes document competition (2018) 0.00 
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    Source
    Information processing and management. 54(2018) no.2, S.252-272
  13. Dadashkarimia, J.; Shakery, A.; Failia, H.; Zamani, H.: ¬An expectation-maximization algorithm for query translation based on pseudo-relevant documents (2017) 0.00 
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    Source
    Information processing and management. 53(2017) no.2, S.371-387

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  • d 2

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