22%OFF
Stock image for illustration purposes only - book cover, edition or condition may vary.
Numerical Algorithms for Personalized Search in Self-Organizing Information Networks
Sep Kamvar
€ 63.77
€ 49.62
FREE Delivery in Ireland
Description for Numerical Algorithms for Personalized Search in Self-Organizing Information Networks
Hardback. 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.
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 ... Read more
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 ... Read more
Product Details
Format
Hardback
Publication date
2010
Publisher
Princeton University Press United States
Number of pages
160
Condition
New
Number of Pages
160
Place of Publication
New Jersey, United States
ISBN
9780691145037
SKU
V9780691145037
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-1
About Sep Kamvar
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 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 "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 "Kamvar helped ... Read more