Numerical Algorithms for Personalized Search in Self-organizing Information Networks

— —

Sep Kamvar

Numerical Algorithms for Personalized Search in Self-organizing Information Networks

Sep Kamvar

$105

Hardback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Princeton University Press
27 September 2010

Internet searching; Computer science; Maths for computer scientists

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.

By: Sep Kamvar
Imprint: Princeton University Press
Country of Publication: United States
Dimensions: Height: 235mm, Width: 152mm, Spine: 11mm
Weight: 397g
ISBN: 9780691145037
ISBN 10: 0691145032
Pages: 160
Publication Date: 27 September 2010
Audience: College/higher education , Professional and scholarly , General/trade , Primary , Undergraduate
Format: Hardback
Publisher's Status: Active

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.

Reviews for Numerical Algorithms for Personalized Search in Self-organizing Information Networks

The clarity of presentation makes this book accessible to a broad audience. The scholarship is thorough and sound, and the experimental results are presented in a precise and detailed fashion. --Taher Haveliwala, QForge Labs The writing style is extremely clear, and the book is accessible to readers both within and outside of the field. --Chen Greif, University of British Columbia Kamvar helped establish a foundation for P2P search and this book provides an authoritative record and source for his excellent work in this area. --Andrew Tomkins, Google