ISBN10: 0691145032, ISBN13: 9780691145037, [publisher: Princeton University Press] Hardcover Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
[Dallas, TX, U.S.A.] [Publication Year: 2010]

Princeton University Press, Date: 2010. Hardcover. Good. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.Dust jacket quality is not guaranteed. 2010. Princeton University Press ISBN 0691145032 9780691145037 [US]

ISBN10: 0691145032, ISBN13: 9780691145037, [publisher: Princeton University Press] Hardcover New Book. Shipped from UK. Established seller since 2000.
[Fairford, GLOS, United Kingdom] [Publication Year: 2010]

ISBN10: 0691145032, ISBN13: 9780691145037, [publisher: Princeton University Press] Hardcover New Book. Shipped from UK. Established seller since 2000.
[Wood Dale, IL, U.S.A.] [Publication Year: 2010]

Princeton University Press 10/8/2010 12: 00: 00 AM Hardcover New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the UK.

Princeton University Press 10/8/2010 12: 00: 00 AM Hardcover PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Please note we cannot offer an expedited shipping service from the UK.

Hardback. New. Lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. This book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. ISBN 0691145032 9780691145037 [GB]

ISBN10: 0691145032, ISBN13: 9780691145037, [publisher: Princeton University Press] Hardcover New copy - Usually dispatched within 4 working days. Lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. This book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data.
[Southport, United Kingdom] [Publication Year: 2010]

Princeton Princeton University Press 2010 Hard cover New. Sewn binding. Cloth over boards. 160 p. Contains: Illustrations, black & white.

Princeton University Press 2010 Hard cover New Lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. This book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Num Pages: 160 pages, 55 line illus. 11 tables. BIC Classification: UY. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 165 x 241 x 16. Weight in Grams: 352. 2010. Hardcover.....We ship daily from our Bookshop.

ISBN10: 0691145032, ISBN13: 9780691145037, [publisher: Princeton University Press] Hardcover nach der Bestellung gedruckt Neuware - Printed after ordering - 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 quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion ch ...
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Princeton Princeton University Press 2010 Hard cover New. Sewn binding. Cloth over boards. 160 p. Contains: Illustrations, black & white.

Princeton, N.J. |Oxford Princeton University Press 2010 Hard cover New in new dust jacket.

ISBN10: 0691145032, ISBN13: 9780691145037, [publisher: Princeton University Press, New Jersey] Hardcover Hardcover. 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 quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks.He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the b ...