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Collective Intelligence in Action Paperback – November 7, 2008

21 customer reviews
ISBN-13: 978-1933988313 ISBN-10: 1933988312 Edition: 1st

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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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Product Details

  • 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: 4.4 out of 5 stars  See all reviews (21 customer reviews)
  • Amazon Best Sellers Rank: #1,184,360 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

22 of 22 people found the following review helpful By Daniel Lemire on November 25, 2008
Format: Paperback
I was recently asked by the publisher to review Collective Intelligence in Action. The author is Satnam Alag, a Bay area engineer with a Ph.D. from the University of California, Berkeley. Dr. Alag is VP of NextBio, a specialized search engine.

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.
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19 of 20 people found the following review helpful By Sopan M. Shewale on November 12, 2008
Format: Paperback
I was not surprised to see "Hello, Sopan. We have recommendations for you" line at the top
when I logged into the site. Yes, this kind of functionality is very easy to
implement into your application after reading Satnam's Collective Intelligence in Action

Have you ever wonder how Netflix is able to recommend movies, what are the latest trends
in the making search more intelligent or how you can intelligently gather new content and
present it to your application?

In this book, Santnam does an excellent job providing the answers to all these questions
The book covers the wide breadth of the topics with amazing focus and detail-architecture
for adding intelligence, tagging and tag clouds, content aggregation through focused web
crawling and from the blogospare, leveraging machine learning techniques such as clustering
and predictive modeling, intelligent search and building recommendation engine.

I particularly liked the approach to explain the mathematical concepts with simple examples,
followed by implementing it in simple Java and then leveraging open-source software.

This book can be very useful if you are interested in integrating different Open Source Softwares
to deliver Enterprise Class Application.

I also liked the authors style of providing summary at the end of each chapter.
He also provides huge set of very useful resources for reading further on the topics
covered into the chapters.

You must pickup this book if you are

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11 of 11 people found the following review helpful By L. King on January 16, 2010
Format: Paperback
To really understand this book one would probably have to be a Java programmer, which I'm not, but I was able to follow the argumentation. I do have some background with data mining using SAS and SQL and the mathematics described are fairly easy to understand for someone with even a 1st year engineering or applied math background. I also have an interest in linguistics which kept me going.

The basic idea is that one can catalog documents by removing irrelevant words (adjectives, abstract pronouns, conjunctives) and "stemming" the remaining words (ie: reducing "sews", "sewing", "resew", "sewer" to a root "sew") and creating a vector containing each root word and the word frequency and then normalizing it. One simple result is the ability to produce "word clouds". Similarity between documents is measured by taking the dot product of the two vectors. Any document compared to itself would have a dot product of 1. Two documents with no common stem words would have a dot product of zero. Similar docs would have a high value close to 1, say .8. Dissimilar docs would have a low coefficient, say .15. Even mistaking "sewer" (a conduit for waste) and sewer (one who uses a needle and thread) is taken into account because both docs would only be similar on a couple of keywords, and dissimilar on most others.

What's really neat is how this information gets collected and can be applied. Social networking sites, including the one you are reading right now,, collect data on us through our choices. Browse for a book while logged on then that's something you are interested in. Approve a review the words in the review, summary of the book and the title counts towards your interests. Disapprove and that counts against your interests.
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