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I could be misreading it, but I believe the premise is "more traction => more action". If I'm right, the idea is that the algorithm dynamically weights the "di
by charliepark 15y ago
I could be misreading it, but I believe the premise is "more traction => more action".
If I'm right, the idea is that the algorithm dynamically weights the "display frequency" of the two (or n) options. So as one of the a/b options shows itself to be more successful, it's shown more frequently. Because the test is self-correcting, you as an A/B test runner don't have to decide when the results are significant enough, and the program itself will automatically choose the more successful option.
- noelwelsh 15y agoThat's correct.
- datadon 15y agoGoing purely off of your boiled down description, this seems like a more advanced version of Genetify (https://github.com/gregdingle/genetify/wiki https://github.com/gregdingle/genetify/wiki). @noelwelsh - care to comment on now what you're doing at Untyped differs from what Genetify offers?
- StavrosK 15y agoThat looks like it uses genetic algorithms, which is much less optimal and more exploratory than bandit algorithms. What noelwelsh is proposing would lead to better results much more quickly and without random permutations of elements.
- noelwelsh 15y agoI agree :)
- e-dard 15y agoThe difference in performance between using evolutionary approaches and bandit algorithms is complex and problem dependent. There is no "one algorithm is better than the other". I've used both approaches for a different application and neither dominate the other.