Search (78 results, page 1 of 4)

  • × theme_ss:"Retrievalalgorithmen"
  1. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.03
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
  2. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.03
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    Date
    31. 5.2004 19:22:06
  3. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.03
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    Date
    16.11.2008 16:22:48
  4. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.03
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    Date
    11. 6.2012 14:22:34
  5. Wechsler, M.; Schäuble, P.: ¬The probability ranking principle revisited (2000) 0.02
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  6. 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
  7. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.02
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    Abstract
    Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
    Object
    P-Rank
  8. Perry, R.; Willett, P.: ¬A revies of the use of inverted files for best match searching in information retrieval systems (1983) 0.02
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  9. 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
  10. 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
  11. Jascó, P.: Mapping algorithms to translate natural language questions into search queries for Web databases (1997) 0.02
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  12. Robertson, S.E.: ¬The probability ranking principle in IR (1977) 0.02
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willet. San Francisco: Morgan Kaufmann 1997. S.281-286.
  13. Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988) 0.02
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.323-328.
  14. Sparck Jones, K.: Search term relevance weighting given little relevance information (1979) 0.02
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.329-338.
  15. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:44
  16. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:56
  17. Bilal, D.: Ranking, relevance judgment, and precision of information retrieval on children's queries : evaluation of Google, Yahoo!, Bing, Yahoo! Kids, and ask Kids (2012) 0.01
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    Abstract
    This study employed benchmarking and intellectual relevance judgment in evaluating Google, Yahoo!, Bing, Yahoo! Kids, and Ask Kids on 30 queries that children formulated to find information for specific tasks. Retrieved hits on given queries were benchmarked to Google's and Yahoo! Kids' top-five ranked hits retrieved. Relevancy of hits was judged on a graded scale; precision was calculated using the precision-at-ten metric (P@10). Yahoo! and Bing produced a similar percentage in hit overlap with Google (nearly 30%), but differed in the ranking of hits. Ask Kids retrieved 11% in hit overlap with Google versus 3% by Yahoo! Kids. The engines retrieved 26 hits across query clusters that overlapped with Yahoo! Kids' top-five ranked hits. Precision (P) that the engines produced across the queries was P = 0.48 for relevant hits, and P = 0.28 for partially relevant hits. Precision by Ask Kids was P = 0.44 for relevant hits versus P = 0.21 by Yahoo! Kids. Bing produced the highest total precision (TP) of relevant hits (TP = 0.86) across the queries, and Yahoo! Kids yielded the lowest (TP = 0.47). Average precision (AP) of relevant hits was AP = 0.56 by leading engines versus AP = 0.29 by small engines. In contrast, average precision of partially relevant hits was AP = 0.83 by small engines versus AP = 0.33 by leading engines. Average precision of relevant hits across the engines was highest on two-word queries and lowest on one-word queries. Google performed best on natural language queries; Bing did the same (P = 0.69) on two-word queries. The findings have implications for search engine ranking algorithms, relevance theory, search engine design, research design, and information literacy.
  18. Willett, P.: Best-match text retrieval (1993) 0.01
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  19. Robertson, S.E.: OKAPI at TREC-1 (1994) 0.01
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    Type
    p
  20. Fox, E.; Betrabet, S.; Koushik, M.; Lee, W.: Extended Boolean models (1992) 0.01
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    Abstract
    The classical interpretation of Boolean operators in an information retrieval system is in general too strict. A standard Boolean query rarely comes close to retrieving all and only those documents which are relevant to a query. Many models have been proposed with the aim of softening the interpretation of the Boolean operators in order to improve the precision and recall of the search results. This chapter discusses 3 such models: the Mixed Min and Max (MMM), the Paice, and the P-noem models. The MMM and Paice models are essentially variations of the classical fuzzy-set model, while the P-norm scheme is a distance-based approach. Our experimental results indicate that each of the above models provide better performance than the classical Boolean model in terms of retrieval effectiveness

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