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  • × theme_ss:"Informetrie"
  1. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.04 
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    Abstract
    This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures
    Date
    22. 1.2011 13:02:21
  2. Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.04 
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    Abstract
    In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing.
    Date
    22. 3.2009 17:49:11
  3. Nicholls, P.T.: Empirical validation of Lotka's law (1986) 0.03 
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    Source
    Information processing and management. 22(1986), S.417-419
  4. Nicolaisen, J.: Citation analysis (2007) 0.03 
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    Date
    13. 7.2008 19:53:22
  5. Fiala, J.: Information flood : fiction and reality (1987) 0.03 
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    Source
    Thermochimica acta. 110(1987), S.11-22
  6. Wolfram, D.: Applied informetrics for information retrieval research (2003) 0.03 
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    Abstract
    The author demonstrates how informetric analysis of information retrieval system content and use provides valuable insights that have applications for the modelling, design, and evaluation of information retrieval systems.
  7. Glänzel, W.: Bibliometrics-aided retrieval - where information retrieval meets scientometrics (2015) 0.03 
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    Footnote
    Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"
  8. Su, Y.; Han, L.-F.: ¬A new literature growth model : variable exponential growth law of literature (1998) 0.03 
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    Date
    22. 5.1999 19:22:35
  9. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.03 
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    Date
    22. 7.2006 15:22:28
  10. Diodato, V.: Dictionary of bibliometrics (1994) 0.02 
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    Footnote
    Rez. in: Journal of library and information science 22(1996) no.2, S.116-117 (L.C. Smith)
  11. Bookstein, A.: Informetric distributions : I. Unified overview (1990) 0.02 
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    Date
    22. 7.2006 18:55:29
  12. Bookstein, A.: Informetric distributions : II. Resilience to ambiguity (1990) 0.02 
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    Date
    22. 7.2006 18:55:55
  13. Ahlgren, P.; Jarneving, B.; Rousseau, R.: Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient (2003) 0.02 
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    Abstract
    Ahlgren, Jarneving, and. Rousseau review accepted procedures for author co-citation analysis first pointing out that since in the raw data matrix the row and column values are identical i,e, the co-citation count of two authors, there is no clear choice for diagonal values. They suggest the number of times an author has been co-cited with himself excluding self citation rather than the common treatment as zeros or as missing values. When the matrix is converted to a similarity matrix the normal procedure is to create a matrix of Pearson's r coefficients between data vectors. Ranking by r and by co-citation frequency and by intuition can easily yield three different orders. It would seem necessary that the adding of zeros to the matrix will not affect the value or the relative order of similarity measures but it is shown that this is not the case with Pearson's r. Using 913 bibliographic descriptions form the Web of Science of articles form JASIS and Scientometrics, authors names were extracted, edited and 12 information retrieval authors and 12 bibliometric authors each from the top 100 most cited were selected. Co-citation and r value (diagonal elements treated as missing) matrices were constructed, and then reconstructed in expanded form. Adding zeros can both change the r value and the ordering of the authors based upon that value. A chi-squared distance measure would not violate these requirements, nor would the cosine coefficient. It is also argued that co-citation data is ordinal data since there is no assurance of an absolute zero number of co-citations, and thus Pearson is not appropriate. The number of ties in co-citation data make the use of the Spearman rank order coefficient problematic.
    Date
    9. 7.2006 10:22:35
  14. White, H. D.: Co-cited author retrieval and relevance theory : examples from the humanities (2015) 0.02 
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    Footnote
    Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"
  15. Mayr, P.; Scharnhorst, A.: Scientometrics and information retrieval - weak-links revitalized (2015) 0.02 
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    Footnote
    Editorial zu einem Special Issue "Combining bibliometrics and information retrieval"
  16. Wolfram, D.: ¬The symbiotic relationship between information retrieval and informetrics (2015) 0.02 
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    Footnote
    Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"
  17. Abbasi, M. K.; Frommholz, I.: Cluster-based polyrepresentation as science modelling approach for information retrieval (2015) 0.02 
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    Footnote
    Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"
  18. Lewison, G.: ¬The work of the Bibliometrics Research Group (City University) and associates (2005) 0.02 
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    Date
    20. 1.2007 17:02:22
  19. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.02 
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    Date
    18. 3.2014 19:13:22
  20. Della Mea, V.; Demartini, G.; Di Gaspero, L.; Mizzaro, S.: Measuring retrieval effectiveness with Average Distance Measure (ADM) (2006) 0.02 
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    Abstract
    Most common effectiveness measures for information retrieval systems are based on the assumptions of binary relevance (either a document is relevant to a given query or it is not) and binary retrieval (either a document is retrieved or it is not). In this paper, we describe an information retrieval effectiveness measure named ADM (Average Distance Measure) that questions these assumptions. We compare ADM with other measures, discuss it from a conceptual point of view, and report some experimental results. Both conceptual analysis and experimental evidence demonstrate ADM adequacy in measuring the effectiveness of information retrieval systems.

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