Download e-book for iPad: Bandit Algorithms for Website Optimization by John Myles White

By John Myles White

ISBN-10: 1449341330

ISBN-13: 9781449341336

John Myles White, "Bandit Algorithms for site Optimization"
2013 | ISBN-10: 1449341330 | writer: O’Reilly Media | PDF | 88 pages | 7 + four MB

When searching for how one can increase your web site, how do you choose which adjustments to make? And which alterations to maintain? This concise ebook indicates you ways to take advantage of Multiarmed Bandit algorithms to degree the real-world price of any differences you are making in your web site. writer John Myles White exhibits you ways this strong type of algorithms might be useful advance web site site visitors, convert viewers to buyers, and raise many different measures of success.

This is the 1st developer-focused booklet on bandit algorithms, that have been formerly defined in simple terms in learn papers. You’ll quick study the advantages of a number of uncomplicated algorithms—including the epsilon-Greedy, Softmax, and higher self assurance sure (UCB) algorithms—by operating via code examples written in Python, which you could simply adapt for deployment by yourself website.
Learn the fundamentals of A/B testing—and realize while it’s larger to exploit bandit algorithms
Develop a unit checking out framework for debugging bandit algorithms
Get extra code examples written in Julia, Ruby, and JavaScript with supplemental on-line fabrics

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Extra resources for Bandit Algorithms for Website Optimization

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We suggest some of these in a list of exercises below. Exercises In order to really build a feel for the epsilon-Greedy algorithm, you need to see how it behaves under a variety of circumstances. To do that, you should try out the following exercises: • Use a different number of arms than the five arms we’ve been working with. See how epsilon-Greedy behaves if there are 2 arms and then see how it behaves if there are 200 arms. • Change the probabilities of reward from the Bernoulli arms. 0? 0? How does it behave when the probabilities for different arms are similar?

The only big thing that’s missing from this approach is that we typically have to work with many arms, so we’ll need to set up an array of Arm objects. shuffle(means) arms = map(lambda (mu): BernoulliArm(mu), means) This will set up an array that contains 5 arms. 4 of them output reward 10% of the time, while the best of them outputs a reward 90% of the time. This is a very black-and-white situation that you won’t see in the real world, but that means that it’s a nice starting point for testing our algorithms.

To convince you that UCB is often very effective despite this counter-intuitive tendency to oscillate back into exploring inferior arms, we need to explicitly compare UCB with the other algorithms we’ve studied so far. This is quite easy to do, because we can simply pool all of the simulation results we’ve gathered so far and treat them like a single unit for analysis. In the next section, we walk through the results. 52 | Chapter 6: UCB – The Upper Confidence Bound Algorithm Comparing Bandit Algorithms Side-by-Side Now that we’ve implemented three different algorithms for solving the Multiarmed Bandit, it’s worth comparing them on a single task.

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Bandit Algorithms for Website Optimization by John Myles White


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