Bandit Algorithms for Website Optimization

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Bandit Algorithms for Website Optimization
Bandit Algorithms for Website Optimization
O’R-lly Media (December 2012) | ISBN: 1449341330 | PDF + EPUB | 88 pages | 10.0 MB

When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success.
This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website.

Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithms
Develop a unit testing framework for debugging bandit algorithms
Get additional code examples written in Julia, Ruby, and javascript with supplemental online materials

Table of Contents

Chapter 1. Two Characters: Exploration and Exploitation
Chapter 2. Why Use Multiarmed Bandit Algorithms?
Chapter 3. The epsilon-Greedy Algorithm
Chapter 4. Debugging Bandit Algorithms
Chapter 5. The Softmax Algorithm
Chapter 6. UCB – The Upper Confidence Bound Algorithm
Chapter 7. Bandits in the Real World: Complexity and Complications
Chapter 8. Conclusion

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