9780691145037 - Numerical Algorithms for Personalized Search in Self-organizing Information Networks de Kamvar, Sep (16 resultados)

Hardcover. Condición: Good. 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!

Hardcover. Condición: Very Good. First Edition. First Edition with number line to 1. Hardcover boards very good with light shelf wear. Spine square. Binding sound. Pages crisp and clean. Interior unmarked.

Condición: New.

Condición: 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. . . . .

Condición: New.

Hardback. Condición: New. 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 book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.

Condición: 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. . . . . Books ship from the US and Ireland.

Condición: As New. Unread book in perfect condition.

Condición: New.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: As New. Unread book in perfect condition.

Hardcover. Condición: Brand New. 139 pages. 9.75x6.50x0.50 inches. In Stock.

Hardback. Condición: New. 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 book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.

Hardcover. Condición: Brand New. 139 pages. 9.75x6.50x0.50 inches. In Stock. This item is printed on demand.

Buch. Condición: Neu. Numerical Algorithms for Personalized Search in Self-organizing Information Networks | Sepandar Kamvar | Buch | Einband - fest (Hardcover) | Englisch | 2010 | Princeton University Press | EAN 9780691145037 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. 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 chapters summarizing results conclude the book's two main sections.Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.