Google's PageRank and Beyond: The Science of Search Engine Rankings

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Princeton University Press, Jul 23, 2006 - Computers - 224 pages

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

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Google's PageRank and beyond: the science of search engine rankings

User Review  - Not Available - Book Verdict

Langville (math, Coll. of Charleston, SC) and Meyer (math, North Carolina State Univ.;Matrix Analysis and Applied Linear Algebra ) examine the logic, mathematics, and sophistication behind Google's ... Read full review

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As a Search Engine Optimization expert with seven years of experience I find this book invaluable.

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About the author (2006)

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.

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