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| Programming Collective Intelligence: Building Smart Web 2.0 Applications | 
 enlarge | Author: Toby Segaran
Publisher: O'Reilly Media, Inc.
Category: Book
List Price: $39.99
Buy New: $22.00
You Save: $17.99 (45%)
New (42) Used (8) from $21.39
Avg. Customer Rating:  37 reviews
Sales Rank: 7100
Format: Illustrated
Media: Paperback
Number Of Items: 1
Pages: 360
Shipping Weight (lbs): 1.3
Dimensions (in): 9.1 x 7 x 0.7
ISBN: 0596529325
Dewey Decimal Number: 006.76
EAN: 9780596529321
ASIN: 0596529325
Publication Date: August 16, 2007
Availability: Usually ships in 1-2 business days
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Product Description
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
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| Customer Reviews: Read 32 more reviews...
 Putting Theory into Practice December 18, 2007
73 out of 76 found this review helpful
This book is probably best for those of you who have read the theory, but are not quite sure how to turn that theory into something useful. Or for those who simply hunger for a survey of how machine learning can be applied to the web, and need a non-mathematical introduction.
My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms. Instead of doing what nearly all other authors will do, prove the math behind the backprop training algorithm, he instead mentions what it does, and goes on to present python code that implements the stated goal.
The upside of the approach is clear -- if you know the theory of neural networks, and are not sure how to apply it (or want to see an example of how it can be applied), then this book is great for that. His example of adaptively training a backprop net using only a subset of the nodes in the network was interesting, and I learned from it. Given all the reading I have done over the years on the subject, that was a bit of a surprise for me.
However, don't take this book as being the "end all, be all" for understanding neural networks and their applications. If you need that, you will want to augment this book with writings that cover some of the other network architectures (SOM, hopfield, etc) that are out there. The same goes for the other topics that it covers.
In the end, this book is a great introduction to what is available for those new to machine learning, and shows better than any other book how it applies to Web 2.0. Major strengths of this book are its broad coverage, and the practicality of its contents. It is a great book for those who are struggling with the theory, and/or those who need to see an example of how the theory can be applied in a concise, practical way.
To the author: I expect this book will get a second edition, as the premise behind the book is such a good one. If that happens, perhaps beef up the equations a bit in the appendix, and cite some references or a bibliography for those readers interested in some more in depth reading about the theory behind all these wonderful techniques. (The lack of a bibliography is why I gave it 4 stars out of 5, I really think that those who are new to the subject would benefit greatly from knowing what sits on your bookshelf.)
 Accessible introduction to complex topics August 17, 2007
48 out of 49 found this review helpful
Segaran has done an excellent job of explaining complex algorithms and mathematical concepts with clear examples and code that is both easy to read and useful. His coding style in Python often reads as clearly as pseudo-code in algorithm books. The examples give real-world grounding to abstract concepts like collaborative filtering and bayesian classification.
My favorite part is how he shows us code (gives it to us!) that goes out into the world, grabs masses of data and does interesting things with it. The use of a hierarchical clustering algorithm to dig into people's intrinsic desires in life as expressed in zebo is worth the price of the book alone. The graph that shows a strong connection between "wife", "kids", and "home" but a different connection between "husband", "children", and "job" is IMHO just fascinating.
Gems like that make this book worth reading cover to cover. After that it can happily hang out on your shelf as a reference anytime you need to build something to mine user data and extract the wisdom of crowds.
Understanding the logic behind sites like Amazon and Google... October 20, 2007
31 out of 32 found this review helpful
Have you ever wondered how some of those "collective intelligence" sites work? How Amazon can suggest books that you'll like based on your browsing history? How a search engine can rank and filter results? Toby Segaran does a very good job in revealing and teaching those types of algorithms in his book Programming Collective Intelligence: Building Smart Web 2.0 Applications. While I'm not ready to run out and build my own version of Facebook now, at least I can start to understand how sites like that are designed.
Contents:
Introduction to Collective Intelligence; Making Recommendations; Discovering Groups; Searching and Ranking; Optimization; Document Filtering; Modeling with Decision Trees; Building Price Models; Advanced Classification - Kernel Methods and SVMs; Finding Independent Features; Evolving Intelligence; Algorithm Summary; Third-Party Libraries; Mathematical Formulas; Index
In each of the chapters, Segaran takes a type of capability, be it decision-making or filtering, and shows how a programming language can be used to build that feature. His examples are all in Python, so it helps if you are already familiar with that language if you want to actually work with the code. But even if you don't know Python, the examples are clear and detailed enough that you can follow along and get the gist of what's happening. I personally think that it would help immensely if you had a background in mathematics and statistics. You can use the code here without having a detailed understanding of math, but I'm sure much of this would be more deeply appreciated if you already know about such things as Tanimoto similarity scores, Euclidean distances, or Pearson coefficients.
From my perspective (a non-Python programmer *without* the math background), I was more interested in understanding the overall picture about things like how ranking systems work or how recommendation engines are structured. While there was more detail than I needed (or understood), I still felt as if I accomplished my goal. I have a much greater appreciation for what companies like Google and Amazon have done in building web applications that allow the knowledge and wisdom of groups to be gathered and applied to my own preferences.
Statistical programmers will probably find years of entertainment here. :) "Normal" programmers will expand their horizons, too.
A "hands-on" approach to an otherwise abstract topic August 16, 2007
17 out of 19 found this review helpful
"Programming Collective Intelligence" is a great book. I took a college course on data mining and this book really would have come in handy.
From a "hands-on" programming perspective, the information on the useful libraries in python for crawling, parsing RSS feeds, python drawing, and accessing popular RESTful APIs are really valuable. The code samples are well documented and rather timely. I think Toby has done an amazingly cogent job of demonstrating the nuts and bolts of implementing the plethora of data mining and AI-related concepts pertinent to the field of Collective Intelligence. Additionally, I was new to Python and this book was a real eye opener.
In fact, more than just a book on Collective Intelligence, this is a really useful Python book. I learned a lot about Python reading through the examples and trying to get them to work on my laptop. (I was new to Python before this book, but have since started using Python at my work).
The author has demystified the abstract idea of Collective Intelligence and presented the concepts in an excellent programming language choice in Python. Most of the topics covered are things most developers just hear about. Taking a college course on Data Mining or Artificial Intelligence may expose one to the ideas, but I have never encountered a book that introduced the topics covered in "Programming Collective Intelligence" in a way so intuitive and familiar to the programmer. Distilling all of the topics into a set of very useful Python script really illustrated how practical and available these concepts really are in ones daily work. I will definitely make use of Toby's book.
A pragmatic and bright approach to a fascinating subject August 28, 2007
17 out of 19 found this review helpful
"Collective Intelligence" is a masterpiece on a subject that is difficult to approach unless you enjoy reading highly specialized papers.
The subject is extremely interesting in the anytime/anywhere information age where data mining technologies and smart algorithms are shaping the way we experience our "digital" lives.
It is hard to get up from the chair and walk away from the computer before finishing reading (and experiencing by coding/playing around with the so interesting examples in the book), like in a good thriller you just want to devour the information to the end and start experimenting yourself with all the new skills that this book will bring for sure to most of its readers.
I have to say that this has been a 2in1 book as I was not familiar with Python. I am amazed now by the variety of libraries and the power of this language to do almost everything your imagination can bring. Both subjects together in the same book have made me to enjoy so much.
Also, as a PhD candidate on Evolutionary Computation I cannot be happier. At last I can see a book with an excellent pragmatic approach and a "hands-on" philosophy which is, in fact, the best way to learn almost anything.
Very useful indeed and it will definitely become permanent part of my most select library.
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