Programming Collective Intelligence: Building Smart Web 2.0 Applications

This was a great book! I highly recommend it! Have fun!
http://www.lwsfreedom.com/id/greentitan
Merry Christmas!!!

This is a visionary book because it predicts a lot of what will happen to the Internet soon. How do we process information in the Internet age? Instead of reading magazines and newspapers we use blogs as our source of news. This is because blogs offer much more customized news feed. In a typical newspaper, how much of its content is of interest to a reader? I guess half is a big value but typically it is less than that.

I start my working day with consuming two sweet drinks. One drink is a cup of coffee. Another is a virtual information soup made of 100 blogs. I glance over most of the stories quickly using Google Reader and select those that I am interested in. I might read them in greater detail later on during the day, in the evening, or on a weekend. I do not know which drink gives me more pleasure - the delicious cup of coffee or sweet virtual soup. I like the latter a lot because it is rich with media content - with bright images, cool videos, wow-type web pages.

However, I often discover news that I wish I found out earlier. In other words, there are so many news sources that reading them all or just looking at the headlines of major blogs will take too much time. We need targeted information delivery service.

This is the main idea of this book. In fact, it starts with explaining how to make recommendations given a set of preferences of a number of people and your own preferences. What are those cool things that you have not tried out yet but everybody else did? The example described in the book is applied to Delicious which does not offer recommendations yet.

I often try to decide what my interests are. The blogs that I am reading might answer this question if one builds groups of them. In fact, I have done this manually, but I found out that this categorization is not perfect. The book answers this question in Chapter 3.

After that the book deviates into a number of additional topics such as search, neural networks, discrete optimization. The author Toby Segaran has a great ability to explain difficult concepts using simple words and pictures. As most of the stuff was familiar to me I was wondering how easy a new concept seemed and how much time I spent originally understanding it.

After that the main melody of the book is there again - the next chapter explains how to filter documents, for example to decide if a particular news story is interesting to you or not. Then the book deviates again into decision trees and building price models and even matching people on a dating site. However, there comes our melody again - this time it explains how to extract trends from a lot of news sources, that is decide what people are discussing today. This feature is similar to Google News except that the user has no control of news sources.

I was surprised when I found out that Python is such a popular language in a scientific community. The book describes lots of libraries dealing with numerical data or displaying various charts. The book will serve as a great introduction to Python language even though there are lots of introductory books available. In fact, learning Python this way it easier and more enjoyable.

After reading the book I definitely want to try out the tricks explained there and improve my information soup. This book is my virtual cookbook.

In short: this book isn't worth its price.

The major part of the volume of the book is code and corresponding explanations. If the reader is a decent programmer, he can actually figure it all out by himself given algorithms. Otherwise it makes more sense to get a book on data structures, or Python, or general algorithm construction and learn the basics there.

The algorithms/methods presented in this book are not really specific to "collaborative intelligence" (with a couple of exceptions). The author gives a brief overview of the techniques and then dives into great details on how to implement it. In reality unless you are working on a toy site, you don't really need that code, since it wouldn't scale or fit the production environment. You'll need the math model / algorithm to come up with reasonable implementation. However, it's exactly what the book is missing. Well, it gives *some* info on that, but you'll need to read a more comprehensive source if you intend to really implement it.

I was quite disappointed with the book. I guess it might be ok for a junior developer to get a feel of what that all is about. If you've ever come up with an algorithm by looking at a mathematical description of the approach, you don't need this book at all; you can write a similar one yourself.

I got more from this book than I have from any other book I read in the past couple of years!
It covers in a streamlined form a huge array of algorithms powering the contemporary web - from recomendation engines to a search engine that includes as one of its features the Google PageRank algorithm, to some quite recent AI innovations.
Just about the only area that was not covered was statistical machine translation. I wish he had done that, since that is my favourite subject.
It helps you see the world through the "Collective Intelligence opportunities" prism.

If you are interested in this topic, you should read this book. Disclaimer: I am new to the topic but appreciate when it is done well and need to understand how to implement it for my job. I was blown away by both the conceptual coverage and the implementation details. This book will allow you to cover the concepts on a first pass then come back and actually build the approaches you are most interested in. Even if you ultimately use a vendor product for recommendation, you will understand the algorithms being used and their proper application and where they are deficient.