Search (8 results, page 1 of 1)

0.023569433 = product of:
  0.047138866 = sum of:
    0.047138866 = product of:
      0.09427773 = sum of:
        0.09427773 = weight(_text_:22 in 3445) [ClassicSimilarity], result of:
          0.09427773 = score(doc=3445,freq=2.0), product of:
            0.17405282 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049703393 = queryNorm
            0.5416616 = fieldWeight in 3445, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=3445)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

25. 8.2005 17:42:22

0.020966778 = product of:
  0.041933555 = sum of:
    0.041933555 = product of:
      0.16773422 = sum of:
        0.16773422 = weight(_text_:authors in 3161) [ClassicSimilarity], result of:
          0.16773422 = score(doc=3161,freq=12.0), product of:
            0.22658853 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.049703393 = queryNorm
            0.7402591 = fieldWeight in 3161, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=3161)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

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.

0.013468248 = product of:
  0.026936496 = sum of:
    0.026936496 = product of:
      0.05387299 = sum of:
        0.05387299 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
          0.05387299 = score(doc=1484,freq=2.0), product of:
            0.17405282 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049703393 = queryNorm
            0.30952093 = fieldWeight in 1484, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1484)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

13. 9.2014 14:45:22

0.0117847165 = product of:
  0.023569433 = sum of:
    0.023569433 = product of:
      0.047138866 = sum of:
        0.047138866 = weight(_text_:22 in 3276) [ClassicSimilarity], result of:
          0.047138866 = score(doc=3276,freq=2.0), product of:
            0.17405282 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049703393 = queryNorm
            0.2708308 = fieldWeight in 3276, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3276)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

20. 3.2005 16:23:22

0.010101185 = product of:
  0.02020237 = sum of:
    0.02020237 = product of:
      0.04040474 = sum of:
        0.04040474 = weight(_text_:22 in 2717) [ClassicSimilarity], result of:
          0.04040474 = score(doc=2717,freq=2.0), product of:
            0.17405282 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049703393 = queryNorm
            0.23214069 = fieldWeight in 2717, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2717)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

11. 9.2004 17:32:22

0.008559652 = product of:
  0.017119303 = sum of:
    0.017119303 = product of:
      0.06847721 = sum of:
        0.06847721 = weight(_text_:authors in 5777) [ClassicSimilarity], result of:
          0.06847721 = score(doc=5777,freq=2.0), product of:
            0.22658853 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.049703393 = queryNorm
            0.30220953 = fieldWeight in 5777, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=5777)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

This book discusses many of the key design issues for building search engines and emphazises the important role that applied mathematics can play in improving information retrieval. The authors discuss not only important data structures, algorithms, and software but also user-centered issues such as interfaces, manual indexing, and document preparation. They also present some of the current problems in information retrieval that many not be familiar to applied mathematicians and computer scientists and some of the driving computational methods (SVD, SDD) for automated conceptual indexing

0.008070117 = product of:
  0.016140234 = sum of:
    0.016140234 = product of:
      0.064560935 = sum of:
        0.064560935 = weight(_text_:authors in 7) [ClassicSimilarity], result of:
          0.064560935 = score(doc=7,freq=4.0), product of:
            0.22658853 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.049703393 = queryNorm
            0.28492588 = fieldWeight in 7, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.03125 = fieldNorm(doc=7)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on link-structure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.

0.004279826 = product of:
  0.008559652 = sum of:
    0.008559652 = product of:
      0.034238607 = sum of:
        0.034238607 = weight(_text_:authors in 6) [ClassicSimilarity], result of:
          0.034238607 = score(doc=6,freq=2.0), product of:
            0.22658853 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.049703393 = queryNorm
            0.15110476 = fieldWeight in 6, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0234375 = fieldNorm(doc=6)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, "Google's PageRank and Beyond" supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. It includes: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and complete and self-contained section for mathematics review.