Bandit Algorithms for Website Optimization

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