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Programming Collective Intelligence Praise for Programming Collective Intelligence “I review a few books each year, and naturally, I read a fair number during the course of my work. And I have to admit that I have never had quite as much fun reading a preprint of a book as I have in reading this. 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, Uber Tech Lead, 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 used directly to analyze social interaction across the Web today. If I had this book two years ago, it would have saved me precious time going down some fruitless paths.” — Tim Wolters, CTO, Collective Intellect “Programming Collective Intelligence is a stellar achievement in providing a comprehensive collection of computational methods for relating vast amounts of data. Specifically, it applies these techniques in context of the Internet, finding value in otherwise isolated data islands. If you develop for the Internet, this book is a must-have.” — Paul Tyma, Senior Software Engineer, Google Programming Collective Intelligence Other resources from O’Reilly Related titles Web 2.0 Report AI for Game Developers Learning Python Mastering Algorithms with Mastering Algorithms with C Perl oreilly.com oreilly.com is more than a complete catalog of O’Reilly books. You’ll also find links to news, events, articles, weblogs, sample chapters, and code examples. oreillynet.com is the essential portal for developers interested in open and emerging technologies, including new platforms, pro- gramming languages, and operating systems. Conferences O’Reilly brings diverse innovators together to nurture the ideas that spark revolutionary industries. We specialize in document- ing the latest tools and systems, translating the innovator’s knowledge into useful skills for those in the trenches. Visit conferences.oreilly.com for our upcoming events. Safari Bookshelf (safari.oreilly.com) is the premier online refer- ence library for programmers and IT professionals. Conduct searches across more than 1,000 books. Subscribers can zero in on answers to time-critical questions in a matter of seconds. Read the books on your Bookshelf from cover to cover or sim- ply flip to the page you need. Try it today for free. Programming Collective Intelligence Building Smart Web 2.0 Applications Toby Segaran Beijing • Cambridge • Farnham • Köln • Paris • Sebastopol • Taipei • Tokyo Programming Collective Intelligence by Toby Segaran Copyright © 2007 Toby Segaran. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (safari.oreilly.com). For more information, contact our corporate/institutional sales department: (800) 998-9938 or [email protected]. Editor: Mary Treseler O’Brien Indexer: Julie Hawks Production Editor: Sarah Schneider Cover Designer: Karen Montgomery Copyeditor: Amy Thomson Interior Designer: David Futato Proofreader: Sarah Schneider Illustrators: Robert Romano and Jessamyn Read Printing History: August 2007: First Edition. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. Programming Collective Intelligence, the image of King penguins, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. This book uses RepKover™, a durable and flexible lay-flat binding. ISBN-10: 0-596-52932-5 ISBN-13: 978-0-596-52932-1 [M] Table of Contents Preface . xiii 1. Introduction to Collective Intelligence . 1 What Is Collective Intelligence? 2 What Is Machine Learning? 3 Limits of Machine Learning 4 Real-Life Examples 5 Other Uses for Learning Algorithms 5 2. Making Recommendations . 7 Collaborative Filtering 7 Collecting Preferences 8 Finding Similar Users 9 Recommending Items 15 Matching Products 17 Building a del.icio.us Link Recommender 19 Item-Based Filtering 22 Using the MovieLens Dataset 25 User-Based or Item-Based Filtering? 27 Exercises 28 3. Discovering Groups . 29 Supervised versus Unsupervised Learning 29 Word Vectors 30 Hierarchical Clustering 33 Drawing the Dendrogram 38 Column Clustering 40 vii K-Means Clustering 42 Clusters of Preferences 44 Viewing Data in Two Dimensions 49 Other Things to Cluster 53 Exercises 53 4. Searching and Ranking . 54 What’s in a Search Engine? 54 A Simple Crawler 56 Building the Index 58 Querying 63 Content-Based Ranking 64 Using Inbound Links 69 Learning from Clicks 74 Exercises 84 5. Optimization . 86 Group Travel 87 Representing Solutions 88 The Cost Function 89 Random Searching 91 Hill Climbing 92 Simulated Annealing 95 Genetic Algorithms 97 Real Flight Searches 101 Optimizing for Preferences 106 Network Visualization 110 Other Possibilities 115 Exercises 116 6. Document Filtering . 117 Filtering Spam 117 Documents and Words 118 Training the Classifier 119 Calculating Probabilities 121 A Naïve Classifier 123 The Fisher Method 127 Persisting the Trained Classifiers 132 Filtering Blog Feeds 134 viii | Table of Contents Improving Feature Detection 136 Using Akismet 138 Alternative Methods 139 Exercises 140 7. Modeling with Decision Trees . 142 Predicting Signups 142 Introducing Decision Trees 144 Training the Tree 145 Choosing the Best Split 147 Recursive Tree Building 149 Displaying the Tree 151 Classifying New Observations 153 Pruning the Tree 154 Dealing with Missing Data 156 Dealing with Numerical Outcomes 158 Modeling Home Prices 158 Modeling “Hotness” 161 When to Use Decision Trees 164 Exercises 165 8. Building Price Models . 167 Building a Sample Dataset 167 k-Nearest Neighbors 169 Weighted Neighbors 172 Cross-Validation 176 Heterogeneous Variables 178 Optimizing the Scale 181 Uneven Distributions 183 Using Real Data—the eBay API 189 When to Use k-Nearest Neighbors 195 Exercises 196 9. Advanced Classification: Kernel Methods and SVMs . 197 Matchmaker Dataset 197 Difficulties with the Data 199 Basic Linear Classification 202 Categorical Features 205 Scaling the Data 209 Table of Contents | ix Understanding Kernel Methods 211 Support-Vector Machines 215 Using LIBSVM 217 Matching on Facebook 219 Exercises 225 10. Finding Independent Features . 226 A Corpus of News 227 Previous Approaches 231 Non-Negative Matrix Factorization 232 Displaying the Results 240 Using Stock Market Data 243 Exercises 248 11. Evolving Intelligence . 250 What Is Genetic Programming? 250 Programs As Trees 253 Creating the Initial Population 257 Testing a Solution 259 Mutating Programs 260 Crossover 263 Building the Environment 265 A Simple Game 268 Further Possibilities 273 Exercises 276 12. Algorithm Summary . 277 Bayesian Classifier 277 Decision Tree Classifier 281 Neural Networks 285 Support-Vector Machines 289 k-Nearest Neighbors 293 Clustering 296 Multidimensional Scaling 300 Non-Negative Matrix Factorization 302 Optimization 304 x | Table of Contents A. Third-Party Libraries . 309 B. Mathematical Formulas . 316 Index . 323 Table of Contents | xi Preface1 The increasing number of people contributing to the Internet, either deliberately or incidentally, has created a huge set of data that gives us millions of potential insights into user experience, marketing, personal tastes, and human behavior in general. This book provides an introduction to the emerging field of collective intelligence.It covers ways to get hold of interesting datasets from many web sites you’ve probably heard of, ideas on how to collect data from users of your own applications, and many different ways to analyze and understand the data once you’ve found it. This book’s goal is to take you beyond simple database-backed applications and teach you how to write smarter programs to take advantage of the information you and others collect every day. Prerequisites The code examples in this book are written in Python, and familiarity with Python programming will help, but I provide explanations of all the algorithms so that pro- grammers of other languages can follow. The Python code will be particularly easy to follow for those who know high-level languages like Ruby or Perl. This book is not intended as a guide for learning programming, so it’s important that you’ve done enough coding to be familiar with the basic concepts. If you have a good understand- ing of recursion and some basic functional programming, you’ll find the material even easier. This book does not assume you have any prior knowledge of data analysis, machine learning, or statistics. I’ve tried to explain mathematical concepts in as simple a manner as possible, but having some knowledge of trigonometry and basic statistics will be help you understand the algorithms. xiii Style of Examples The code examples in each section are written in a tutorial style, which encourages you to build the applications in stages and get a good appreciation for how the algo- rithms work. In most cases, after creating a new function or method, you’ll use it in an interactive session to understand how it works. The algorithms are mostly simple variants that can be extended in many ways. By working through the examples and testing them interactively, you’ll get insights into ways that you might improve them for your own applications. Why Python? Although the algorithms are described in words with explanations of the formulae involved, it’s much more useful (and probably easier to follow) to have actual code for the algorithms and example problems.
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