Search (2 results, page 1 of 1)

  • × author_ss:"Willett, P."
  • × theme_ss:"Retrievalalgorithmen"
  • × type_ss:"a"
  1. Li, J.; Willett, P.: ArticleRank : a PageRank-based alternative to numbers of citations for analysing citation networks (2009) 0.00 
    0.0048465277 = product of:
  0.033925693 = sum of:
    0.033925693 = weight(_text_:based in 751) [ClassicSimilarity], result of:
      0.033925693 = score(doc=751,freq=6.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.28829288 = fieldWeight in 751, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=751)
  0.14285715 = coord(1/7)
    Abstract
    Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database. Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value - This is a novel application of the PageRank algorithm.
  2. Robertson, M.; Willett, P.: ¬An upperbound to the performance of ranked output searching : optimal weighting of query terms using a genetic algorithms (1996) 0.00 
    0.00447703 = product of:
  0.03133921 = sum of:
    0.03133921 = weight(_text_:based in 6977) [ClassicSimilarity], result of:
      0.03133921 = score(doc=6977,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.26631355 = fieldWeight in 6977, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0625 = fieldNorm(doc=6977)
  0.14285715 = coord(1/7)
    Abstract
    Describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson-Sparck Jones F4 retrospective relevance weight

Authors

Years