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内容説明
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.
著者について
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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40 人中、38人の方が、｢このレビューが参考になった｣と投票しています。
surveys search techniques
2006/8/17
形式:ハードカバー
Langville and Meyer have done a superb job describing both Google's technical foundations, and the broader subject of how search engines rank pages. Over half the book is devoted to explaining the maths and rationales behind PageRank. The level of maths is understandable to those who have done some university level courses on linear algebra (i.e. matrices).
The book also has considerable value in analysing what other organisations (like search engines) and researchers have cobbled together. It gives a useful summation of the state of the research, circa 2006. Essentially, everyone seems to focus on link analysis, after Google revolutionised the industry in 1998 by using this. It blew away the previous leader, AltaVista.
It is true, as the authors point out, that most of the material here has already been published. But as discrete events, scattered through various scientific journals and websites. You can certainly get explanations of PageRank on several websites. But the mathematical depth and reliability of those discussions can vary with the site. The book is far handier.
It is a good starting point, if you are interesting in devising your own search methods.
The book also has considerable value in analysing what other organisations (like search engines) and researchers have cobbled together. It gives a useful summation of the state of the research, circa 2006. Essentially, everyone seems to focus on link analysis, after Google revolutionised the industry in 1998 by using this. It blew away the previous leader, AltaVista.
It is true, as the authors point out, that most of the material here has already been published. But as discrete events, scattered through various scientific journals and websites. You can certainly get explanations of PageRank on several websites. But the mathematical depth and reliability of those discussions can vary with the site. The book is far handier.
It is a good starting point, if you are interesting in devising your own search methods.
11 人中、10人の方が、｢このレビューが参考になった｣と投票しています。
practical and fun
2007/1/19
形式:ハードカバー
Great work! I wish I read it before I start my Ph.D. study.
Pros:
1) Precise and intuitive description of the search algorithm
2) Plenty of interesting stories making mathematics fully applicable in practice
3) Sample Matlab code available
Cons:
This is actually a perfect book. But one needs to have basic linear algebra to appreciate its value. If you are looking for "SEO", you are in a wrong spot.
But if anyone wonder how Page and Brin turn math into treasure, read it!
Pros:
1) Precise and intuitive description of the search algorithm
2) Plenty of interesting stories making mathematics fully applicable in practice
3) Sample Matlab code available
Cons:
This is actually a perfect book. But one needs to have basic linear algebra to appreciate its value. If you are looking for "SEO", you are in a wrong spot.
But if anyone wonder how Page and Brin turn math into treasure, read it!
14 人中、12人の方が、｢このレビューが参考になった｣と投票しています。
The maths of google
2007/9/25
形式:ハードカバー
The subtitle "The science of search engine rankings" is a misnomer. This book is primarily about the *mathematics* of pagerank. For non-mathematicians, such as a computer scientist like myself (though I do have undergrad maths), it was pretty slow going and just plain boring.
I wanted algorithm examples for pagerank calculation of largish (10M) data sets. Not matlab code. Matlab might be great for people who love matrices and don't mind being locked-in to a proprietary language, but it is hardly a sensible choice for a production implementation of the pagerank algorithm. And an algorithm using matrix manipulation, while it might be mathematically nice, is difficult to implement efficiently without fancy matrix compression tricks (as far as I can tell).
In the end, I discarded the book, and wrote my own shorter, simpler, non-matrix implementation in python, verified it produced the same results, and then rewrote it in C. It is quite fast enough for 10M pages even without any fancy optimisations. Not a matrix in sight. Yay.
For mathematicians, this book might deserve more than 3 stars. For computer scientists though, I wouldn't recommend it.
I wanted algorithm examples for pagerank calculation of largish (10M) data sets. Not matlab code. Matlab might be great for people who love matrices and don't mind being locked-in to a proprietary language, but it is hardly a sensible choice for a production implementation of the pagerank algorithm. And an algorithm using matrix manipulation, while it might be mathematically nice, is difficult to implement efficiently without fancy matrix compression tricks (as far as I can tell).
In the end, I discarded the book, and wrote my own shorter, simpler, non-matrix implementation in python, verified it produced the same results, and then rewrote it in C. It is quite fast enough for 10M pages even without any fancy optimisations. Not a matrix in sight. Yay.
For mathematicians, this book might deserve more than 3 stars. For computer scientists though, I wouldn't recommend it.
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