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Bandit Algorithms for Website Optimization

Book Description

This book shows you how to run experiments on your website using A/B testing—and then takes you a huge step further by introducing you to bandit algorithms for website optimization. Author John Myles White shows you how this family of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success.

Table of Contents

  1. Bandit Algorithms for Website Optimization
  2. Preface
    1. Finding the Code for This Book
    2. Dealing with Jargon: A Glossary
    3. Conventions Used in This Book
    4. Using Code Examples
    5. Safari® Books Online
    6. How to Contact Us
    7. Acknowledgments
  3. 1. Two Characters: Exploration and Exploitation
    1. The Scientist and the Businessman
      1. Cynthia the Scientist
      2. Bob the Businessman
      3. Oscar the Operations Researcher
    2. The Explore-Exploit Dilemma
  4. 2. Why Use Multiarmed Bandit Algorithms?
    1. What Are We Trying to Do?
    2. The Business Scientist: Web-Scale A/B Testing
  5. 3. The epsilon-Greedy Algorithm
    1. Introducing the epsilon-Greedy Algorithm
    2. Describing Our Logo-Choosing Problem Abstractly
      1. What’s an Arm?
      2. What’s a Reward?
      3. What’s a Bandit Problem?
    3. Implementing the epsilon-Greedy Algorithm
    4. Thinking Critically about the epsilon-Greedy Algorithm
  6. 4. Debugging Bandit Algorithms
    1. Monte Carlo Simulations Are Like Unit Tests for Bandit Algorithms
    2. Simulating the Arms of a Bandit Problem
    3. Analyzing Results from a Monte Carlo Study
      1. Approach 1: Track the Probability of Choosing the Best Arm
      2. Approach 2: Track the Average Reward at Each Point in Time
      3. Approach 3: Track the Cumulative Reward at Each Point in Time
    4. Exercises
  7. 5. The Softmax Algorithm
    1. Introducing the Softmax Algorithm
    2. Implementing the Softmax Algorithm
    3. Measuring the Performance of the Softmax Algorithm
    4. The Annealing Softmax Algorithm
    5. Exercises
  8. 6. UCB – The Upper Confidence Bound Algorithm
    1. Introducing the UCB Algorithm
    2. Implementing UCB
    3. Comparing Bandit Algorithms Side-by-Side
    4. Exercises
  9. 7. Bandits in the Real World: Complexity and Complications
    1. A/A Testing
    2. Running Concurrent Experiments
    3. Continuous Experimentation vs. Periodic Testing
    4. Bad Metrics of Success
    5. Scaling Problems with Good Metrics of Success
    6. Intelligent Initialization of Values
    7. Running Better Simulations
    8. Moving Worlds
    9. Correlated Bandits
    10. Contextual Bandits
    11. Implementing Bandit Algorithms at Scale
  10. 8. Conclusion
    1. Learning Life Lessons from Bandit Algorithms
    2. A Taxonomy of Bandit Algorithms
    3. Learning More and Other Topics
  11. Colophon
  12. Copyright