If you’re Google or Netflix, and you have a recommendation or search system as part of your bread and butter, what’s the best way to test improvements to your algorithm? A/B testing is the canonical answer for testing how users respond to software changes, but it gets tricky really fast to think about what an A/B test means in the context of an algorithm that returns a ranked list. That’s why we’re talking about interleaving this week—it’s a simple modification to A/B testing that makes it much easier to race two algorithms against each other and find the winner, and it allows you to do it with much less data than a traditional A/B test.
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