You are previewing Bandit Algorithms for Website Optimization.

Bandit Algorithms for Website Optimization

Cover of Bandit Algorithms for Website Optimization by John Myles White Published by O'Reilly Media, Inc.
  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

Chapter 2. Why Use Multiarmed Bandit Algorithms?

What Are We Trying to Do?

In the previous chapter, we introduced the two core concepts of exploration and exploitation. In this chapter, we want to make those concepts more concrete by explaining how they would arise in the specific context of website optimization. When we talk about "optimizing a website", we’re referring to a step-by-step process in which a web developer makes a series of changes to a website, each of which is meant to increase the success of that site. For many web developers, the most famous type of website optimization is called Search Engine Optimization (or SEO for short), a process that involves modifying a website to increase that site’s rank in search engine results. We won’t discuss SEO at all in this book, but the algorithms that we will describe can be easily applied as part of an SEO campaign in order to decide which SEO techniques work best.

Instead of focusing on SEO, or on any other sort of specific modification you could make to a website to increase its success, we’ll be describing a series of algorithms that allow you to measure the real-world value of any modifications you might make to your site(s).

But, before we can describe those algorithms, we need to make sure that we all mean the same thing when we use the word "success." From now on, we are only going to use the word "success" to describe measurable achievements like:

Did a change increase the amount of traffic to a site’s landing ...

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