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Programming Collective Intelligence: Building Smart Web 2.0 Applications

Programming Collective Intelligence: Building Smart Web 2.0 Applications
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ISBN13: 9780596529321
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Additional Programming Collective Intelligence: Building Smart Web 2.0 Applications Information

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how 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
  • Predicting numerical values rather than classifications to build price models
  • Evolving intelligence for problem solving--how a computer develops its skill by improving its own code the more it plays a gameĀ 
Each chapter includes exercises for extending the algorithms to make predictions, but to model the way decisions are made
  • Using decision trees not only to make predictions, but to model the way decisions are made
  • Bayesian filtering, used in spam filters for classifying documents based on word types and other features
  • Support vector machines to match people in online dating sites
  • Non-negative matrix factorization to find the independent features in If I had this book two years ago, it would have saved precious time going down some fruitless paths."
    -- 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.

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