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Numerical algorithms for personalized search in self-organizing information networks / Sep Kamvar.

By: Material type: TextTextPublication details: Princeton : Princeton University Press, ©2010.Description: 1 online resource (xiv, 139 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781400837069
  • 1400837065
Subject(s): Additional physical formats: Print version:: Numerical algorithms for personalized search in self-organizing information networks.DDC classification:
  • 025.5/24 22
LOC classification:
  • ZA4460 .K36 2010eb
Online resources:
Contents:
Numerical Algorithms for Personalized Search in Self-organizing Information Networks; Contents; Tables; Figures; Acknowledgments; Chapter 1 Introduction; 1.1 World Wide Web; 1.2 P2P Networks; 1.3 Contributions; PART I WORLD WIDE WEB; Chapter 2 PageRank; Chapter 3 The Second Eigenvalue of the Google Matrix; Chapter 4 The Condition Number of the PageRank Problem; Chapter 5 Extrapolation Algorithms; Chapter 6 Adaptive PageRank; Chapter 7 BlockRank; PART II P2P NETWORKS; Chapter 8 Query-Cycle Simulator; Chapter 9 EigenTrust; Chapter 10 Adaptive P2P Topologies; Chapter 11 Conclusion; Bibliography.
Summary: This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quad.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBSCO Computers Available
Total holds: 0

Includes bibliographical references (pages 135-139).

Numerical Algorithms for Personalized Search in Self-organizing Information Networks; Contents; Tables; Figures; Acknowledgments; Chapter 1 Introduction; 1.1 World Wide Web; 1.2 P2P Networks; 1.3 Contributions; PART I WORLD WIDE WEB; Chapter 2 PageRank; Chapter 3 The Second Eigenvalue of the Google Matrix; Chapter 4 The Condition Number of the PageRank Problem; Chapter 5 Extrapolation Algorithms; Chapter 6 Adaptive PageRank; Chapter 7 BlockRank; PART II P2P NETWORKS; Chapter 8 Query-Cycle Simulator; Chapter 9 EigenTrust; Chapter 10 Adaptive P2P Topologies; Chapter 11 Conclusion; Bibliography.

This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quad.

Print version record.

Added to collection customer.56279.3

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