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Collective Intelligence in Action Paperback – 7 Nov 2008

4.5 out of 5 stars 2 customer reviews

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Product details

  • Paperback: 425 pages
  • Publisher: Manning Publications; 1 edition (7 Nov. 2008)
  • Language: English
  • ISBN-10: 1933988312
  • ISBN-13: 978-1933988313
  • Product Dimensions: 18.7 x 2.4 x 23.5 cm
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 1,027,574 in Books (See Top 100 in Books)

Product Description

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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Format: Paperback
This book covers a lot of ground. The bibiography gets 5 stars, despite the fact that some of the URL's are already dead. I've posted a topic "URL related stuff" on the Manning Forum for the book. There is a good blend of case studies and code written in Java, harnessing things like Lucene, Nutch, WEKA, Java Data Mining. The book deals with ways of harvesting information from the Blogosphere as well as how to implement tag clouds and recommendation engines like Amazon's own system for books. It also deals with web crawling and how to do things like predictive modelling. It was this area which for me lost a star. I consider myself to be reasonably mathematically savvy, although some of this stuff I've not used in anger since my A Level days some 20+ years ago. But I found I couldn't keep up with some of chapters 9 & 10. I'll have to augment the book by referencing some of the citations. The way I'll endeavour to comprehend some of the code is running it through a debugger and re-reading the chapters again. It's not that the code is unclear. In fact I'd say the opposite. It's just the most practical way for me to get to grips with the heavy maths.
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The book is relatively old (I read it back in 2009 while working on my MSc dissertation), therefore I assume that some information and terms (i.e. Web 2.0, blogosphere, etc.) will be dated, but the theory and principles behind the topics discussed in the book (especially the chapter on recommendation systems) should still be valid.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 4.4 out of 5 stars 21 reviews
22 of 22 people found the following review helpful
5.0 out of 5 stars A must for all Web engineers 25 Nov. 2008
By Daniel Lemire - Published on Amazon.com
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.
20 of 21 people found the following review helpful
5.0 out of 5 stars Adding Smartness made easy in your Enterprise Application by "Collective Intelligence in Action" 12 Nov. 2008
By Sopan M. Shewale - Published on Amazon.com
Format: Paperback
I was not surprised to see "Hello, Sopan. We have recommendations for you" line at the top
when I logged into the Amazon.com 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

[1]. serious (developer/manager/architect type of Eng) on adding search or
intelligent/smartness into your Application
[2]. person involved in developing (programmer, tester, manager) Social
Networking Application.
[3]. involved in managing "Knowledge Management Infrastructure" of any size organization

This Book will provide you a great foundation for developing Enterprise Class
Features.

I highly recommend it.
11 of 11 people found the following review helpful
5.0 out of 5 stars Fascinating book about how Web 2.0 sites work. 17 Jan. 2010
By L. King - Published on Amazon.com
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, Amazon.com, 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. Write a review and the words you write become part of your cumulative profile as well, reduced to a vector or vectors of keywords and frequencies.

Here's how it gets applied: One of Amazon's marketing tools is it's "recommendation engine". (The book talks about Netflix recommendation engine and business model). By matching your vector against other people who have bought/viewed what you have bought a prediction can be made as to the likelihood of you being interested in the something that they have bought, or not interested in items that they rejected or disliked. The more Amazon caters to what you are interested in, and doesn't bother you with irrelevancies, the happier you may be.

Other applications discussed include the automatic creation of folksonomies (taxonomies based on popular usage) using cluster analysis and categorization using Bayes theorem.

In addition to recommendation engines Alag points out the usefulness of these techniques to Search and points out several search engines that apply this approach (as does Google), tools that search out and provide news based on your preferences, or suggest "friends" (ie: Facebook or eHarmony might use these ideas), search for similar material to identify copyright infringement, email filters that keep out spam for rolex watches or viagra (unless you are interested in rolex watches or viagra), construct a virus detection engine based on code phrases or early detection of epidemics or adverse reactions to medication through similarities in medical reports. Alag himself appears to be working at a biotech firm NextBio that matches public medical and genome related data to data held by private companies.

Some of the basic tools discussed are Lucene, a free version of what Google will sell you for a search engine, Nutch, a free web crawler, both of which require coding and WEKA, a free open source data mining package that looks usable by the rest of us.

Loved the book and the author's organization of the material. Some of the social implications are scary, especially for privacy concerns, but so is the implication of not leveraging the information that one holds within your organization to provide the best possible service. For example the World Bank has the capability (not necessarily using these methods) to match similar projects around the world so that experience gained in one area can be found and applied elsewhere. This is a key fast moving tech that one needs to understand in order to see where we are going as a society. C.I. in Action is merely the opening salvo - the methods and techniques described are the basics but there is much room for refinement and elaboration and this topic could be the start of a whole new field. The book also recommends and has sparked my interest in the site [...] which is probably more accessible to someone without a math or tech background.

Finally a note to SF fans, esp. of Spider Robinson's Callahan's Crosstime Saloon series, this may be the point at which the Web starts to appear to be intelligent. :-)
12 of 14 people found the following review helpful
2.0 out of 5 stars A lot of ideas, but neither theoretical enough nor pratical enough 20 July 2010
By G. Webster - Published on Amazon.com
Format: Paperback
This book contains a lot of ideas and as such is a good starting point for further reading. But it's not a one-stop resource for actually implementing the algorithms it mentions, as a lot of them are described only in a very high level and incomplete way. For example, in the discussion of model-based recommendation engines in sections 12.3.3-12.3.5, the author gives a very short description of latent semantic indexing (LSI) and some Java code that shows how to use the Weka implementation. But firstly, the description is too short to give the user a real understanding of what is going on theoretically. And secondly, the implementation description doesn't go nearly far enough: it shows that reconstructing the original matrix from the top N dimensions of the singular value decomposition gives a close approximation to the original, but then it just stops there; it doesn't explain how to actually use the decomposition in a recommendation engine. And the section on LSI is verbose compared to the "section" on Bayesian belief networks, which at a single paragraph of text is completely inadequate for either practical or theoretical purposes. And so on throughout the book.
8 of 10 people found the following review helpful
3.0 out of 5 stars Collective Intelligence in Action 11 Nov. 2009
By Eric Jain - Published on Amazon.com
Format: Paperback
This book is more deserving of the "Collective Intelligence" title than O'Reilly's "Programming Collective Intelligence" as it's not just about algorithms, but discusses blogs, wikis etc, and shows how to do basic implementations of features such as tag clouds or finding related content in that context. Instead of explaining specific algorithms in detail, existing Java libraries are used, e.g. WEKA for data mining and Lucene for search.

There are lots of diagrams, and (somewhat verbose) Java code. The examples in this book are good starting points for further exploration; this book is more about showing what can be done and getting you started in the right direction than providing you with an understanding of the algorithms (as does the O'Reilly book) and libraries that are used.
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