A01=Amy N. Langville
A01=Carl D. Meyer
Addition
Adjacency matrix
Algorithm
AltaVista
Analysis of algorithms
Anchor text
Approximation
Author_Amy N. Langville
Author_Carl D. Meyer
Blog
Calculation
Category=UDB
Category=UDBR
Category=UNH
Coefficient matrix
Computation
Condition number
Connected component (graph theory)
Directed graph
Eigenvalues and eigenvectors
Email
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Extrapolation
Goal
Google matrix
Google News
HITS algorithm
Home page
HTML
Hyperlink
Information retrieval
Instance (computer science)
Irreducibility (mathematics)
Iteration
Iterative method
Larry Page
Linear algebra
Link analysis
Link farm
Markov chain
Mathematics
MATLAB
Matrix (mathematics)
Meta element
Metasearch engine
Numerical analysis
Online diary
PageRank
Parameter (computer programming)
Permutation matrix
Personalization
Power iteration
Probability
Proportionality (mathematics)
Ranking (information retrieval)
Rate of convergence
Result
Scientific notation
Search engine optimization
Sergey Brin
Spamdexing
Spamming
Sparse matrix
Stochastic
Stochastic matrix
Subset
Summation
Technology
Teoma
Theorem
Web crawler
Web page
Web search engine
Webmaster
Website
World Wide Web
Product details
- ISBN 9780691152660
- Weight: 482g
- Dimensions: 178 x 254mm
- Publication Date: 26 Feb 2012
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Paperback
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Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of web page rankings, Google's PageRank and Beyond supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text.
Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. * Many illustrative examples and entertaining asides * MATLAB code * Accessible and informal style * Complete and self-contained section for mathematics review
Amy N. Langville is Assistant Professor of Mathematics at the College of Charleston in Charleston, South Carolina. She studies mathematical algorithms for information retrieval and text and data mining applications. Carl D. Meyer is Professor of Mathematics at North Carolina State University. In addition to information retrieval, his research areas include numerical analysis, linear algebra, and Markov chains. He is the author of Matrix Analysis and Applied Linear Algebra.
