Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in adataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Customer Rating:
Summary: mind blower
Comment: This is one of those books I wish I had more time to devote to. I've barely begun to read it and already, I'm thrilled with the information being shared - I never knew what I didn't know, but this book has really opened my eyes to an entire facet of my development expertise that needs to improve.

Highly recommended

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Customer Rating:
Summary: Nice introduction to exciting topics but lacks depth
Comment: I think this is a good, easy-to-read intro to several interesting data-centric software technologies, but it is superficial.

For example, their collaborative filtering (ratings + recommendations) section illustrates only the most simplest of algorithms and completely skips over more advanced techniques (improved normalization, matrix factorization, and others), it skips over even basic benchmarking of the rec system (IMO, if you aren't doing objective benchmarks and tuning it off of those metrics, your rec system is useless), and doesn't address any of the common pitfalls and problems (sparsity, overfitting, normalization problems, scalability issues).

I guess that is expected. If you want a book that's easy to read that can get you excited about some cool ares in software development, this book is great. If you want information beyond the introductory casual reading level, look elsewhere.

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Customer Rating:
Summary: A fantastic book full of ideas & examples for anyone developing against websites with large user bases
Comment: As a long time O'Reilly reader & fan, I have to say this is the best O'Reilly book I've
read in the past several years, and is now among my favorite programming books in general. This is really an applied Artificial Intelligence book in disguise, as it covers most of the core topics found amongst the top AI textbooks. I've recently read a few of the standard AI books, such as Norvig, Duda & Hart; which are thorough, but in a bad way, because they miss the forest for the trees. Your average working software developer is not going to be able to use these textbooks to create any code without investing a lot of time, or stopping long the way to get a Phd.

And this is precisely where this books shines, unlike similar books out there--Toby Segaran has managed to explain the core AI algorithms in plain language, with very readable code examples that implement a fully working example to get you started. Reading this book made me realize most of the AI that I've studied is not hard in itself, but rather the standard way AI algorithms are presented in textbooks is just terrible and obfuscated.

For example, Toby describes a fully working backpropagation neural network, with code(!) in about 9 pages. I've never seen a NN presentation better than this. There were several chapters where I couldn't help laughing at how conceptually easy a given algorithm ends up being if only you stop and explain it as simply as possible, and throw out most of the mathematical notation. That sounds obvious, but for some reason few authors think brevity helps get the point across, especially when dealing with a mathematical topic. So kudos to Toby for this, which is a major accomplishment in itself, as it's going to really help the book appeal to a much wider audience.

I also though it was a great idea to connect every topic in the book to large data sets which anyone can get off the web. This lead me to think of many other kinds of datasets to try this code on, so it's not the kind of book that you read and put away;
but rather you keep tweaking the example code(available on the book's website), adding to it and experimenting.

In all, a great book, highly recommended!

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Customer Rating:
Summary: Excellent Resource for Clustering Algorithoms and Other AI Algorithoms
Comment: I use python as my primary programming language, when I ordered this book I was concerned it would be more about website design then AI algorithms (collective intelligence encompasses a subset of soft AI algorithms that draw upon information from various sources readily avaliable on the Internet, large document collections, etc.) I found the text to be readable with broad application in other areas including document classification systems for analyzing large amount of documents in the context of e-discovery. I would recommend this book to anyone using any-type of clustering process for review and analyzing documents and data. Taxonomic, clustering, neural networks, etc. are sold generally to the public as magic while in fact the concepts are readily accessible in this book.

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Customer Rating:
Summary: Great working examples
Comment: Love the book, great topical review of methods with working examples. Every chapter makes you think of a dozen things you could do next.
My only reason for 4 instead of 5 stars is that the code examples are all python-based and leverage python specific features. The book title should be "Programming Collective Intelligence...with Python" although it does present a fun challenge to convert the examples to a different language (like Ruby!).

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