Google's PageRank and Beyond

书名：Google's PageRank and BeyondTheScienceofSearchEngineRankings
作者：AmyN.Langville/CarlD.Meyer
译者：
ISBN：9780691122021
出版社：PrincetonUniversityPress
出版时间：2006-7-23
格式：epub/mobi/azw3/pdf
页数：240
豆瓣评分： 9.2

书籍简介：

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.

作者简介：

书友短评：

@ C6H5NO2 迭代算法求矩阵特征向量。 @ Nova 科普书，没什么意思～ @ naomielie happy reading @ ISpring 对于pagerank的机制介绍得很详细，并附有很多实验所需的实用信息。此书是对深入研究的很好的引子。 @ confabulator 看了前面几章，不错的入门参考书。 @ confabulator 看了前面几章，不错的入门参考书。 @ ISpring 对于pagerank的机制介绍得很详细，并附有很多实验所需的实用信息。此书是对深入研究的很好的引子。 @ Nova 科普书，没什么意思～ @ naomielie happy reading @ C6H5NO2 迭代算法求矩阵特征向量。

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