- Paperback: 425 pages
- Publisher: Manning Publications; 1 edition (November 7, 2008)
- Language: English
- ISBN-10: 1933988312
- ISBN-13: 978-1933988313
- Product Dimensions: 7.4 x 0.9 x 9.2 inches
- Shipping Weight: 1.1 pounds (View shipping rates and policies)
- Average Customer Review: 20 customer reviews
- Amazon Best Sellers Rank: #1,457,525 in Books (See Top 100 in Books)
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Collective Intelligence in Action 1st Edition
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About the Author
Satnam Alag, PhD, is currently the Vice President of Engineering at NextBio, a vertical search engine and a Web 2.0 collaboration application for the life sciences community. He is a seasoned software professional with over fifteen years of experience in machine learning and over a decade of experience in commercial software development and management. Dr. Alag worked as a consultant with Johnson & Johnsons's BabyCenter where he helped develop their personalization engine. Prior to that, he was the Chief Software Architect at Rearden Commerce and began his career at GE R&D. He is a Sun Certified Enterprise Architect (SCEA) for the Java Platform. Dr. Alag earned his PhD in engineering from UC Berkeley and his dissertation was in the area of probabilistic reasoning and machine learning. He has published numerous peer-reviewed articles.
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Like any good text book, the material is presented in an iterative and incremental way. High level concepts are introduced first. Popular features such as tag clouds and recommendation engines are defined. How collective intelligence manifests in the GUI and screen flow of various popular web properties is illustrated. Various data structures and algorithms are classified. Simple examples of some of the underlying math is worked out. Open source libraries are recommended and coding examples presented. Topics are introduced simply at first and revisited in later chapters with ever increasing amounts of detail.
However, there's a lot of math in the topic of collective intelligence and this is not a math book. Neither does it take a "heads up" or "for dummies" approach. Instead, it aims somewhere in the middle which gives the book a bit of an identity crisis.
If you're a script jock or a web master whose level of technical competence doesn't extend too far past embedding a youtube video or paypal "buy now" button on your pages, then the most that you are going to get out of this book is just the high level introductory material.
The formally trained software engineer can treat this book as a great introductory survey level course on the subject that does attempt to peal back some of the layers of complexity. It is neither definitive nor canonical so you will be doing more research and studying before you deploy any sophisticated collective intelligence in your applications.
For people who have been working on CI for a while, this book provides great insights on how to use the various concepts to areas such as clustering, classification and building predictive models, and recipes to translate item and user data into meaningful information. Depending on your previous experience though, you may find certain sections of the book redundant.
If I have a complaint, it would be the rather verbose Java code that accompanies the various recipes. The code is written with best practices in mind, so while it is probably directly copy-pasteable into your own code, it is harder to read and takes a bit more time to understand than similar pseudo-code (or code written with readability as its primary objective).
Overall, a very practical and informative book that I think would be useful to both new and experienced CI programmers alike.
The first chapter is free and so is the source code used in the book.
The book is for Java developers who want to implement "Collective Intelligence" applications in Java. It tells us about extracting and applying data from blogs, wikis and social network applications. I am not one to praise, but this book succeeds brilliantly. If you are a Java engineer and work with Web technologies, you must get this book. It covers topics such as computing similarity measures using vector models, Nai've Bayes Classifiers, inverse document frequency (idf), Machine Learning (using the Weka API), building a crawler with regular expressions, collaborative filtering (with links to open source tools), and so on.
Even if you do not work with Java, if you care for high-end Web applications, this book is for you. It reminds me of Lyon's Java¿ Digital Signal Processing book. It offers the gist of what academia knows, but focuses on what people (engineers and researchers) do in practise.
The book is not meant for academia however. There are references, but no theorem.
Disclaimer. I did not get paid to review this book, and I do not stand to gain anything if you buy the book. I have no relationship with the publisher or the author.
Further reading. A competing book is Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran. It uses Python instead of Java.