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Algorithms of the Intelligent Web (Englisch) Taschenbuch – 11. Juni 2009

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HIGHLIGHT Learn the techniques used by Google, Netflix, and Amazon to transform raw data into actionable information - including recommendations, predictions, and intelligent search. DESCRIPTION Web 2.0 applications provide a rich user experience, but the parts you can't see are just as important - and impressive. They use powerful techniques to process information intelligently and offer features based on patterns and relationships in data. Algorithms of the Intelligent Web shows readers how to use the same techniques employed by household names like Google Ad Sense, Netflix, and Amazon to transform raw data into actionable information. Algorithms of the Intelligent Web is an example-driven blueprint for creating applications that collect, analyze, and act on the massive quantities of data users leave in their wake as they use the web. Readers learn to build Netflix-style recommendation engines, and how to apply the same techniques to social-networking sites. See how click-trace analysis can result in smarter ad rotations.

All the examples are designed both to be reused and to illustrate a general technique - an algorithm - that applies to a broad range of scenarios. As they work through the book's many examples, readers learn about recommendation systems, search and ranking, automatic grouping of similar objects, classification of objects, forecasting models, and autonomous agents. They also become familiar with a large number of open-source libraries and SDKs, and freely available APIs from the hottest sites on the internet, such as Facebook, Google, eBay, and Yahoo. KEY POINTS Create recommendations like those on Netflix and Amazon Implement Google's Pagerank and the HITS algorithm Discover matches on social-networking sites Business techniques like sorting email based on content, targeted advertising, and fraud detection MARKET INFORMATION The fields of Collective Intelligence and Web 2.0 are driving much of the interest in new web development techniques. This book is front-and-center in this hot area.

Über den Autor und weitere Mitwirkende

Dr. Haralambos Marmanis

holds a Ph.D. in applied mathematics from Brown

University, an M.S. degree in theoretical and applied mechanics from the

University of Illinois at Urbana-Champaign, and B.S. and M.S. degrees in civil

engineering from the Aristotle University of Thessaloniki in Greece. He was the

recipient of the Sigma Xi award for innovative research in 2000, and he is the

author of numerous publications in peer-reviewed international scientific journals,

conferences, and technical periodicals.

Dmitry Babenko is the lead for the data warehouse infrastructure at Emptoris,

Inc. He is a software engineer and architect with 13 years of experience in the IT

industry. He has designed and built a wide variety of applications and infrastructure

frameworks for banking, insurance, supply-chain management, and business

intelligence companies. He received a M.S. degree in computer science from

Belarussian State University of Informatics and Radioelectronics.


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88 von 93 Kunden fanden die folgende Rezension hilfreich
A soon to be classic Algo book for improving intelligent web applications 19. Juni 2009
Von Michael Mimo - Veröffentlicht auf
Format: Taschenbuch
I have always had an interest in AI, machine learning, and data mining but I found the introductory books too mathematical and focused mostly on solving academic problems rather than real-world industrial problems. So, I was curious to see what this book was about.

I have read the book front-to-back (twice!) before I write this report. I started reading the electronic version a couple of months ago and read the paper print again over the weekend. This is the best practical book in machine learning that you can buy today -- period. All the examples are written in Java and all algorithms are explained in plain English. The writing style is superb! The book was written by one author (Marmanis) while the other one (Babenko) contributed in the source code, so there are no gaps in the narrative; it is engaging, pleasant, and fluent. The author leads the reader from the very introductory concepts to some fairly advanced topics. Some of the topics are covered in the book and some are left as an exercise at the end of each chapter (there is a "To Do" section, which was a wonderful idea!). I did not like some of the figures (they were probably made by the authors not an artist) but this was only a minor aesthetic inconvenience.

The book covers four cornerstones of machine learning and intelligence, i.e. intelligent search, recommendations, clustering, and classification. It also covers a subject that today you can find only in the academic literature, i.e. combination techniques. Combination techniques are very powerful and although the author presents the techniques in the context of classifiers, it is clear that the same can be done for ecommendations -- as the Bell Korr team did for the Netflix prize.

I work in a financial company and a number of people that I work with have PhD degrees in mathematics and computer science. I found the book so fascinating that I asked them to have a look. They had nothing but praise for this book. The consensus is that everything is explained in the simplest possible way, with clarity but without sacrificing accuracy. As one of them told me, this is a major step forward in teaching AI techniques and introducing the field to millions of developers around the world. Even for experts in the field and experienced software engineers, there are important insights in almost every chapter.

We had tried to write a software library, for a small project, that analyzes log files and assesses IT risk (e.g. probability of intrusion; preemptive alerts on application performance issues, and so on) based on Segaran's book "Programming collective intelligence". We spend about six weeks trying to find how to match what was in Segaran's book and what we wanted to do but we did not find the depth and clarity that was required. On top of that, Segaran used Python so the code had to be rewritten and things didn't quite work as expected! We are now using the code from Marmanis' book and our code analyzes apache and weblogic log files in order to assess risk! It just works! We wrote the code in one week! We would not have been able to succeed without reading this book.

Clearly, I am deeply impressed. This is an outstanding book; it was not just useful, it was inspiring! It is a "must have" book for every Java developer.

The content of the book includes:
* the PageRank algorithm; a content based algorithm similar to PageRank to which the author coined the term "DocRank" because it applies to Word, PDF, and other documents rather than Web pages; search improvements based on probabilistic methods (Naive Bayes); precision, recall, F1-score, and ROC curves;
* collaborative filtering as well as content based recommendations;
* k-means, ROCK, DBSCAN for clustering; the best explanation about the "curse of dimensionality" ever! I finally learned what this mystic term means!
* Bayesian classification; declarative programming (through the Drools rules engine); introduction to neural networks; decision trees
* Comparing and Combining classifiers: McNemar's test; Cochran'sQ test; F-test; Bagging; Boosting; general classifier ensembles

Buy it, read it, enjoy it, and use it!
40 von 44 Kunden fanden die folgende Rezension hilfreich
Artfully splits the difference between providing recipes and teaching algorithms 16. August 2009
Von calvinnme - Veröffentlicht auf
Format: Taschenbuch
This is a book that is for the working professional who already knows Java and wants to not only implement intelligent algorithms, he/she wants to understand the theory behind it. All of the code is in Java, so if you don't know this language this book will be over your head. It would also help if you have some background in algorithms along the lines of the material covered in Introduction to Algorithms.

The author is attempting to teach both the algorithms behind the information retrieval that is done on the web and at the same time show those algorithms implemented in Java in such a way that it is clear to the reader what has been done. This approach can be a tricky middle ground often resulting in books that are confusing from both a textbook and from a cookbook standpoint. Fortunately, the author has done a good job of integrating these two viewpoints into a cohesive whole and the result is a book I can heartily recommend. The author makes liberal use of figures and explains what is being done at a high level first, showing pseudocode before actually showing the Java code. Discussions on the inner workings of the algorithms follow.

Note that use is made of higher level libraries such as Lucene when they are available, because this is a book for professionals after all, and your boss would not be pleased if you reinvented the wheel every time you implemented an algorithm. But, don't worry, the explanation behind the code is there too. Another good book that is language agnostic that makes a good companion to this one is Machine Learning (Mcgraw-Hill International Edit). It is an oldie but a goodie.

The product description does not yet show the table of contents so I do that next:

Chapter 1. What is the intelligent web?
Section 1.1. Examples of intelligent web applications
Section 1.2. Basic elements of intelligent applications
Section 1.3. What applications can benefit from intelligence?
Section 1.4. How can I build intelligence in my own application?
Section 1.5. Machine learning, data mining, and all that
Section 1.6. Eight fallacies of intelligent applications
Section 1.7. Summary

Chapter 2. Searching
Section 2.1. Searching with Lucene
Section 2.2. Why search beyond indexing?
Section 2.3. Improving search results based on link analysis
Section 2.4. Improving search results based on user clicks
Section 2.5. Ranking Word, PDF, and other documents without links
Section 2.6. Large-scale implementation issues
Section 2.7. Is what you got what you want? Precision and recall
Section 2.8. Summary
Section 2.9. To do

Chapter 3. Creating suggestions and recommendations
Section 3.1. An online music store: the basic concepts
Section 3.2. How do recommendation engines work?
Section 3.3. Recommending friends, articles, and news stories
Section 3.4. Recommending movies on a site such as[...]
Section 3.5. Large-scale implementation and evaluation issues
Section 3.6. Summary
Section 3.7. To Do

Chapter 4. Clustering: grouping things together
Section 4.1. The need for clustering
Section 4.2. An overview of clustering algorithms
Section 4.3. Link-based algorithms
Section 4.4. The k-means algorithm
Section 4.5. Robust Clustering Using Links (ROCK)
Section 4.6. DBSCAN
Section 4.7. Clustering issues in very large datasets
Section 4.8. Summary
Section 4.9. To Do

Chapter 5. Classification: placing things where they belong
Section 5.1. The need for classification
Section 5.2. An overview of classifiers
Section 5.3. Automatic categorization of emails and spam filtering
Section 5.4. Fraud detection with neural networks
Section 5.5. Are your results credible?
Section 5.6. Classification with very large datasets
Section 5.7. Summary
Section 5.8. To do
Classification schemes
Books and articles

Chapter 6. Combining classifiers
Section 6.1. Credit worthiness: a case study for combining classifiers
Section 6.2. Credit evaluation with a single classifier
Section 6.3. Comparing multiple classifiers on the same data
Section 6.4. Bagging: bootstrap aggregating
Section 6.5. Boosting: an iterative improvement approach
Section 6.6. Summary
Section 6.7. To Do

Chapter 7. Putting it all together: an intelligent news portal
Section 7.1. An overview of the functionality
Section 7.2. Getting and cleansing content
Section 7.3. Searching for news stories
Section 7.4. Assigning news categories
Section 7.5. Building news groups with the NewsProcessor class
Section 7.6. Dynamic content based on the user's ratings
Section 7.7. Summary
Section 7.8. To do

Appendix A. Introduction to BeanShell
Section A.1. What is BeanShell?
Section A.2. Why use BeanShell?
Section A.3. Running BeanShell

Appendix B. Web crawling
Section B.1. An overview of crawler components

Appendix C. Mathematical refresher
Section C.1. Vectors and matrices
Section C.2. Measuring distances
Section C.3. Advanced matrix methods

Appendix D. Natural language processing

Appendix E. Neural networks
43 von 48 Kunden fanden die folgende Rezension hilfreich
Mislead by the other reviews 23. März 2011
Von Russ Abbott - Veröffentlicht auf
Format: Taschenbuch
I selected this book as the text for a course on the basis of the earlier reviews. They sounded so good. Covers the concepts and includes concrete code that does what the concepts intend. But the book didn't live up to the reviews.

First of all, the code uses BeanShell as a way to run the examples. BeanShell is a neat idea. It's one of a number of languages that move Java closer to being a scripting language. But it's not necessary for the book's purposes. It's a bit of a pain to install, and it takes a while to get used to. In the end it's an unnecessary distraction. It's far simpler to run the examples in eclipse with the "scripts" entered as the body of a main() method.

The preceding is a relatively minor point, but in some ways it illustrates some of the problems I had with the book. It focuses too much on the code. Yes, it's nice to have code that does what one is trying to describe, but code is not a substitute for a good explanation. In many places the book provides inadequate descriptions of the concepts, presumably on the grounds that one can just read the code. But code is not tutorial. Code itself must be commented to be understandable. And code cannot replace a good intuitive description of the important ideas.

Furthermore, the code (and the output) take up too much space in the book. There are pages of output when a few lines would suffice, and there are pages of code when a well-constructed paragraph would do. Pearson's coefficient is a good example. There is approximately a page of code to do the calucuation. There is also half a page of code-level comments--e.g., "The method getAverage is self-explanatory; it calculates the average of the vector that's provided as an argument." But there is no straightforward description of what's going on in the computation as a whole.

Further, there are frequent references to other books and papers as if making such references excuse the author from explaining an idea. For example, the Pearson's coefficient discussion includes this sentence. "There's a smarter way to do this that avoids a plague of numerical calculations called the roundoff error; read the article on the corrected two-pass algorithm by Chan, Golub, and LeVeque." That's all that's said about roundoff error or the smarter way to do something or other numerical computation issues. Referring the reader to an article is not good enough. If it's worth discussing, discuss it. If it's not important enough to discuss, then don't refer the reader elsewhere except as enrichment. The author does this over and over.

Another example of what I would consider the book's conceptual superficiality is its treatment of Bayes' Theorem. Bayes' Theorem is mentioned many times, but the only explanation is a half page translation of Bayes' Theorem into words--with no explanation of why Bayes' Theorem is true. Understanding why Bayes' Theorem is true should have been an important lesson.

A similar criticism holds for Decision Trees except even more so. There is no discussion at all about how to construct a decision tree. Such a discussion would have been a perfect place to introduce the notion of entropy. But the word "entropy" doesn't even appear in the book.

All-in-all I found the book disappointing. If one wants to build software that performs some of the functions discussed, the book can help. But if one wants to understand the principles underlying such software, the book is not the right place to go.
16 von 18 Kunden fanden die folgende Rezension hilfreich
Instructive and entertaining review of algorithms and techniques relevant to providing intelligent web apps 8. Dezember 2009
Von Robin Hillyard - Veröffentlicht auf
Format: Taschenbuch
This books is not a "heavy" Artificial Intelligence tome. Instead it is a thought-provoking, instructive and very enjoyable read. It covers many of the everyday problems that web applications face: searching, clustering, relevance, etc.. In general, problems involving large quantities of typically imperfect, multi-dimensional data.

I have been working with these kinds of problems for several decades now and this is one of the best books I've come across. It is particularly relevant to the problems that are typically faced by web application developers in the Web 2.0 era.
2 von 3 Kunden fanden die folgende Rezension hilfreich
Fine tuned text which neatly balances between science and craft 15. September 2012
Von Srecko Gnjidic - Veröffentlicht auf
Format: Taschenbuch
First and foremost, congratulations for authors for such a high quality text and narrative style which artfully slaloms between pop and sci.

While writing the book such as Algorithms of the Intelligent Web, it is not easy to imagine the average reader. The book resounded in me quite well; it seems to me that on my career path I came to the point when such material is striking a chord within me. As a software developer, but the one who never implemented components described in this book - I found it excellent read. Luckily, I finished courses in mathematics and statistics which were greatly benificial while reading, so I could just float on the waves of high quality writing style and enjoy the ride to the final chapter.
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