Numerical Algorithms for Personalized Search in Self-organizing Information Networks

Sepandar Kamvar

ISBN 10: 0691145032 / ISBN 13: 9780691145037

Editore: Princeton University Press, 2010

Riguardo questo articolo

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.

Codice articolo 9780691145037

Dati bibliografici

• Titolo: Numerical Algorithms for Personalized Search in Self-organizing Information Networks
• Editore: Princeton University Press
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 0691145032
• ISBN 13: 9780691145037
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 240 L x 161 A x 13 P (millimetri) 240 millimetri di larghezza per 161 millimetri di altezza per 13 millimetri di profondità
• Peso: 405 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Sep Kamvar is a consulting assistant professor of computational mathematics at Stanford University. From 2003 to 2007, he was the engineering lead for personalization at Google. He is the founder and former CEO of Kaltix, a personalized search engine acquired by Google in 2003.