Great news! A murder victim has been found. No slow news day today! The story is already written, now a title needs to be selected. The clever reporter who wrote the story has come up with two potential titles - "Murder victim found in adult entertainment venue" and "Headless Body found in Topless Bar". (The latter title is one I've shamelessly stolen from the NY Daily News.) Once upon a time, deciding which title to run was a matter for a news editor to decide. Those days are now over - the geeks now rule the earth. Title selection is now primarily an algorithmic problem, not an editorial one.
One common approach is to display both potential versions of the title on the homepage or news feed, and measure the Click Through Rate (CTR) of each version of the title. At some point, when the measured CTR for one title exceeds that of the other title, you'll switch to the one with the highest for all users. Algorithms for solving this problem are called bandit algorithms.
In this blog post I'll describe one of my favorite bandit algorithms, the Bayesian Bandit, and show why it is an excellent method to use for problems which give us more information than typical bandit algorithms.
Unless you are already familiar with Bayesian statistics and beta distributions, I strongly recommend reading the previous blog post. That post provides much introductory material, and I'll depend on it heavily.