Search (2 results, page 1 of 1)

0.011311792 = product of:
  0.033935376 = sum of:
    0.015822036 = product of:
      0.031644072 = sum of:
        0.031644072 = weight(_text_:theory in 6) [ClassicSimilarity], result of:
          0.031644072 = score(doc=6,freq=4.0), product of:
            0.16234003 = queryWeight, product of:
              4.1583924 = idf(docFreq=1878, maxDocs=44218)
              0.03903913 = queryNorm
            0.19492465 = fieldWeight in 6, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.1583924 = idf(docFreq=1878, maxDocs=44218)
              0.0234375 = fieldNorm(doc=6)
      0.5 = coord(1/2)
    0.018113337 = product of:
      0.036226675 = sum of:
        0.036226675 = weight(_text_:methods in 6) [ClassicSimilarity], result of:
          0.036226675 = score(doc=6,freq=6.0), product of:
            0.15695344 = queryWeight, product of:
              4.0204134 = idf(docFreq=2156, maxDocs=44218)
              0.03903913 = queryNorm
            0.2308116 = fieldWeight in 6, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.0204134 = idf(docFreq=2156, maxDocs=44218)
              0.0234375 = fieldNorm(doc=6)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Content

Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling
Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation

0.0012430967 = product of:
  0.0074585797 = sum of:
    0.0074585797 = product of:
      0.014917159 = sum of:
        0.014917159 = weight(_text_:theory in 2924) [ClassicSimilarity], result of:
          0.014917159 = score(doc=2924,freq=2.0), product of:
            0.16234003 = queryWeight, product of:
              4.1583924 = idf(docFreq=1878, maxDocs=44218)
              0.03903913 = queryNorm
            0.09188836 = fieldWeight in 2924, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.1583924 = idf(docFreq=1878, maxDocs=44218)
              0.015625 = fieldNorm(doc=2924)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Footnote

Weitere Rez. in: New Library World 108(2007) nos.3/4, S.190-191 (K.V. Trickey): "Vanda has provided a very useful work that will enable any reader who is prepared to follow her instruction to produce a thesaurus that will be a quality language-based subject access tool that will make the task of information retrieval easier and more effective. Once again I express my gratitude to Vanda for producing another excellent book." - Electronic Library 24(2006) no.6, S.866-867 (A.G. Smith): "Essential thesaurus construction is an ideal instructional text, with clear bullet point summaries at the ends of sections, and relevant and up to date references, putting thesauri in context with the general theory of information retrieval. But it will also be a valuable reference for any information professional developing or using a controlled vocabulary." - KO 33(2006) no.4, S.215-216 (M.P. Satija)