Search (32 results, page 1 of 2)

  • × theme_ss:"Retrievalalgorithmen"
  1. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.08
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    Abstract
    Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. Proposes an idea to integrate 2 existing techniques, query expansion and relevance feedback to achieve a concept-based information search for the Web
    Date
    1. 8.1996 22:08:06
  2. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  3. 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
  4. 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
  5. Ruthven, T.; Lalmas, M.; Rijsbergen, K.van: Incorporating user research behavior into relevance feedback (2003) 0.02
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    Abstract
    Ruthven, Mounia, and van Rijsbergen rank and select terms for query expansion using information gathered on searcher evaluation behavior. Using the TREC Financial Times and Los Angeles Times collections and search topics from TREC-6 placed in simulated work situations, six student subjects each preformed three searches on an experimental system and three on a control system with instructions to search by natural language expression in any way they found comfortable. Searching was analyzed for behavior differences between experimental and control situations, and for effectiveness and perceptions. In three experiments paired t-tests were the analysis tool with controls being a no relevance feedback system, a standard ranking for automatic expansion system, and a standard ranking for interactive expansion while the experimental systems based ranking upon user information on temporal relevance and partial relevance. Two further experiments compare using user behavior (number assessed relevant and similarity of relevant documents) to choose a query expansion technique against a non-selective technique and finally the effect of providing the user with knowledge of the process. When partial relevance data and time of assessment data are incorporated in term ranking more relevant documents were recovered in fewer iterations, however retrieval effectiveness overall was not improved. The subjects, none-the-less, rated the suggested terms as more useful and used them more heavily. Explanations of what the feedback techniques were doing led to higher use of the techniques.
  6. Xu, B.; Lin, H.; Lin, Y.: Assessment of learning to rank methods for query expansion (2016) 0.02
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  7. Karisani, P.; Rahgozar, M.; Oroumchian, F.: Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system (2016) 0.02
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  8. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:44
  9. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:56
  10. 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
  11. Faloutsos, C.: Signature files (1992) 0.01
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    Date
    7. 5.1999 15:22:48
  12. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01
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    Date
    22. 3.2003 19:27:23
  13. 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
  14. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01
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    Date
    13. 9.2014 14:45:22
  15. 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
  16. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01
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    Date
    20. 3.2005 16:23:22
  17. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.01
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    Date
    12. 9.1996 13:56:22
  18. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.01
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  19. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.01
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    Date
    22. 3.2003 19:27:36
  20. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.01
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    Date
    31. 5.2004 19:22:06

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