Google's Pagerank and Beyond:; the science of search engine rankings

Langville, Amy N; Meyer, C. D.

ISBN 10: 0691122024 / ISBN 13: 9780691122021

Edité par Princeton University Press, Princeton, N.J., 2006

A propos de cet article

Quarto in white DJ, x, 224 pages, b&w illustrations; 26 cm. Web sites -- Ratings and rankings -- Mathematics. Contents: Introduction to web search engines -- Crawling, indexing, and query processing -- Ranking webpages by popularity -- The mathematics of Google's PageRank -- Parameters in the PageRank model -- The sensitivity of PageRank -- The PageRank problem as a linear system -- Issues in large-scale implementation of PageRank -- Accelerating the computation of PageRank -- Updating the PageRank vector -- The HITS method for ranking webpages -- Other link methods for ranking webpages -- The future of web information retrieval -- Resources for web information retrieval -- The mathematics guide. 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. -- Jacket. DJ protected in archival mylar sleeve. Ex-library book with three markings (stamp to front free endpaper, stamp to two fore-edges, and stamp to rear pastedown), DJ taped to boards, very mild rubbing to spine head and foot, very gently rubbed corners, light soiling mark to front free endpaper, bright pages in tight binding, light soiling to DJ, extremely gently rubbed DJ corners, extremely mild rubbing to DJ spine head and foot, sticker to DJ spine, else Very Good(+) in Very Good DJ. First edition, first printing (full number line).

N° de réf. du vendeur 91081

Détails bibliographiques

• Titre: Google's Pagerank and Beyond:; the science of search engine rankings
• Éditeur: Princeton University Press, Princeton, N.J.
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 0691122024
• ISBN à 13 chiffres: 9780691122021
• Reliure: Hardcover
• Édition: Edition originale
• État de l'article: Very Good(+)
• État de la jaquette: Jaquette
• Catalogues du vendeur:
  • Science, Technology & Computers

A propos de ce titre

Synopsis

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 in this title. This title features: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and, complete and self-contained section for mathematics review.

À propos de l?auteur

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".