Numerical Algorithms for Personalized Search in Self-organizing Information Networks

Numerical Algorithms for Personalized Search in Self-organizing Information Networks

by Sep Kamvar
     
 

View All Available Formats & Editions

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

…  See more details below

Overview

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.

Read More

Product Details

ISBN-13:
9781400837069
Publisher:
Princeton University Press
Publication date:
09/07/2010
Sold by:
Barnes & Noble
Format:
NOOK Book
Pages:
160
File size:
6 MB

What People are saying about this

Chen Greif
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
Andrew Tomkins
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
Taher Haveliwala
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

Meet the Author

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.

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >