Google's PageRank and Beyond : : The Science of Search Engine Rankings / / Carl D. Meyer, Amy N. Langville.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Langville, Amy N.,; Meyer, Carl D.,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2011]; ©2006
• Year of Publication: 2011
• Edition: Course Book
• Language: English
• Physical Description: 1 online resource (240 p.) :; 11 halftones. 26 line illus.

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

Table of Contents

Frontmatter -- Contents -- Preface -- Chapter One. Introduction to Web Search Engines -- Chapter Two. Crawling, Indexing, and Query Processing -- Chapter Three. Ranking Webpages by Popularity -- Chapter Four. The Mathematics of Google's PageRank -- Chapter Five. Parameters in the PageRank Model -- Chapter Six. The Sensitivity of PageRank -- Chapter Seven. The PageRank Problem as a Linear System -- Chapter Eight. Issues in Large-Scale Implementation of PageRank -- Chapter Nine. Accelerating the Computation of PageRank -- Chapter Ten. Updating the PageRank Vector -- Chapter Eleven. The HITS Method for Ranking Webpages -- Chapter Twelve. Other Link Methods for Ranking Webpages -- Chapter Thirteen. The Future of Web Information Retrieval -- Chapter Fourteen. Resources for Web Information Retrieval -- Chapter Fifteen. The Mathematics Guide -- Chapter Sixteen. Glossary -- Bibliography -- Index