Satnam Alag's Blog

“Collective Intelligence” book review

Taran Rampersad wrote a good review for the book at http://www.knowprose.com/node/18880.

I have copied the last three paragraphs from Taran's review

"Part three brings it all together in a way such that it is greater than the sum of the two other parts. Code, diagrams and the author's voice are clear and left me wondering what else is going to go into this book this August.

Overall, I am very impressed with this work and even excited about the book - something which may something about myself as much as the book. It is technical, it is theoretical - but most importantly, it is practical and focused throughout a diverse group of disciplines that, through the author, are integrated very well.

Since it is an unedited draft, I will not give the book a formal review at this point. But I will say that it is what many people have been waiting for - an explanation for how these things work. Great stuff. Collective Intelligence in Action is worth an early look - or waiting for".

Thanks Taran for taking the time to look at the book and writing the review.

Differences from other books

The book is really meant for developers (basic level of Java understanding helps) who are looking to add intelligence to their applications, especially user-centric Web 2.0 applications. A lot of work has been done by the open-source community in Java in the areas of text processing and search (Lucene), data mining (WEKA), web crawling (Nutch), and data mining standards (JDM). This book leverages these frameworks; presents examples and develops code that you can directly use in your Java application.  

This is a practical book and I present a holistic view on things required to apply these techniques in the real-world. Consequently, the book discusses the architectures for implementing intelligence – you will find lots of diagrams, especially UML diagrams, lots of screen shots from well-known sites,  in addition to code listings, and even database schema designs.

There are lots of examples. Typically, concepts and the underlying math for algorithms is explained via examples with detailed step-by-step analysis. Accompanying the examples is Java code that demonstrates the concepts by implementing the concept and/or using open-source frameworks.

There are a number of exciting topics that you will find interesting and are typically not covered by other books: harvesting information from the blogosphere, analyzing content – especially user-generated content, intelligent web crawling, intelligent search, building recommendation systems. In the last chapter, I also cover three real-world examples of personalization by Amazon, Google News, and Netflix – the BellKor solution from the Netflix competition is also covered. At the end of this you should be familiar with text analysis using Lucene, web crawling using Nutch, building content-based and collaborative-based recommendation engines, and data mining using WEKA and JDM.

The book is on track to be released in August. In the meantime you can get a copy from the <a href="http://www.manning.com/affiliate/idevaffiliate.php?id=891_113_1_23> Manning website </a> through their early access program.