Programming Collective Intelligence

Programming Collective Intelligence

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 a dataset
• 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.

Contents

1. Chapter 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
2. Chapter 2 Making Recommendations
1. Collaborative Filtering
2. Collecting Preferences
3. Finding Similar Users
4. Recommending Items
5. Matching Products
6. Building a del.icio.us Link Recommender
7. Item-Based Filtering
8. Using the MovieLens Dataset
9. User-Based or Item-Based Filtering?
10. Exercises
3. Chapter 3 Discovering Groups
1. Supervised versus Unsupervised Learning
2. Word Vectors
3. Hierarchical Clustering
4. Drawing the Dendrogram
5. Column Clustering
6. K-Means Clustering
7. Clusters of Preferences
8. Viewing Data in Two Dimensions
9. Other Things to Cluster
10. Exercises
4. Chapter 4 Searching and Ranking
1. What's in a Search Engine?
2. A Simple Crawler
3. Building the Index
4. Querying
5. Content-Based Ranking
6. Using Inbound Links
7. Learning from Clicks
8. Exercises
5. Chapter 5 Optimization
1. Group Travel
2. Representing Solutions
3. The Cost Function
4. Random Searching
5. Hill Climbing
6. Simulated Annealing
7. Genetic Algorithms
8. Real Flight Searches
9. Optimizing for Preferences
10. Network Visualization
11. Other Possibilities
12. Exercises
6. Chapter 6 Document Filtering
1. Filtering Spam
2. Documents and Words
3. Training the Classifier
4. Calculating Probabilities
5. A Naïve Classifier
6. The Fisher Method
7. Persisting the Trained Classifiers
8. Filtering Blog Feeds
9. Improving Feature Detection
10. Using Akismet
11. Alternative Methods
12. Exercises
7. Chapter 7 Modeling with Decision Trees
1. Predicting Signups
2. Introducing Decision Trees
3. Training the Tree
4. Choosing the Best Split
5. Recursive Tree Building
6. Displaying the Tree
7. Classifying New Observations
8. Pruning the Tree
9. Dealing with Missing Data
10. Dealing with Numerical Outcomes
11. Modeling Home Prices
12. Modeling "Hotness"
13. When to Use Decision Trees
14. Exercises
8. Chapter 8 Building Price Models
1. Building a Sample Dataset
2. k-Nearest Neighbors
3. Weighted Neighbors
4. Cross-Validation
5. Heterogeneous Variables
6. Optimizing the Scale
7. Uneven Distributions
8. Using Real Data—the eBay API
9. When to Use k-Nearest Neighbors
10. Exercises
9. Chapter 9 Advanced Classification: Kernel Methods and SVMs
1. Matchmaker Dataset
2. Difficulties with the Data
3. Basic Linear Classification
4. Categorical Features
5. Scaling the Data
6. Understanding Kernel Methods
7. Support-Vector Machines
8. Using LIBSVM
9. Matching on Facebook
10. Exercises
10. Chapter 10 Finding Independent Features
1. A Corpus of News
2. Previous Approaches
3. Non-Negative Matrix Factorization
4. Displaying the Results
5. Using Stock Market Data
6. Exercises
11. Chapter 11 EVOLVING INTELLIGENCE
1. What Is Genetic Programming?
2. Programs As Trees
3. Creating the Initial Population
4. Testing a Solution
5. Mutating Programs
6. Crossover
7. Building the Environment
8. A Simple Game
9. Further Possibilities
10. Exercises
12. Chapter 12 Algorithm Summary
1. Bayesian Classifier
2. Decision Tree Classifier
3. Neural Networks
4. Support-Vector Machines
5. k-Nearest Neighbors
6. Clustering
7. Multidimensional Scaling
8. Non-Negative Matrix Factorization
9. Optimization
1. Appendix Third-Party Libraries
1. Universal Feed Parser
2. Python Imaging Library
3. Beautiful Soup
4. pysqlite
5. NumPy
6. matplotlib
7. pydelicious
2. Appendix Mathematical Formulas
1. Euclidean Distance
2. Pearson Correlation Coefficient
3. Weighted Mean
4. Tanimoto Coefficient
5. Conditional Probability
6. Gini Impurity
7. Entropy
8. Variance
9. Gaussian Function
10. Dot-Products
3. Colophon