Search (92 results, page 1 of 5)

  • × theme_ss:"Retrievalalgorithmen"
  1. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.04
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    Date
    14. 6.2015 22:12:44
    Source
    Deutscher Dokumentartag 1985, Nürnberg, 1.-4.10.1985: Fachinformation: Methodik - Management - Markt; neue Entwicklungen, Berufe, Produkte. Bearb.: H. Strohl-Goebel
  2. Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015) 0.03
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  3. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.03
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    Abstract
    This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
  4. Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.03
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  5. Ding, Y.: Topic-based PageRank on author cocitation networks (2011) 0.03
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    Abstract
    Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.
  6. Zhang, D.; Dong, Y.: ¬An effective algorithm to rank Web resources (2000) 0.03
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  7. Xu, B.; Lin, H.; Lin, Y.: Assessment of learning to rank methods for query expansion (2016) 0.02
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  8. Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001) 0.02
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  9. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.02
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    Date
    1. 8.1996 22:08:06
  10. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.02
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    Date
    20. 3.2005 16:23:22
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
  11. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.02
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    Date
    31. 5.2004 19:22:06
  12. 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
  13. 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
  14. 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
  15. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:56
  16. Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000) 0.01
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  17. Nie, J.-Y.: Query expansion and query translation as logical inference (2003) 0.01
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  18. Xu, Y.; Wang, D.: Order effect in relevance judgment : mediation and causality (2008) 0.01
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  19. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.01
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  20. 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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    Source
    Journal of intelligent information systems [https://doi.org/10.1007/s10844-023-00815-y]

Years

Languages

  • e 67
  • d 24
  • m 1
  • More… Less…

Types

  • a 86
  • m 4
  • el 1
  • r 1
  • s 1
  • x 1
  • More… Less…