I've asked people at Amazon on the recommendation algorithm side about this, since it comes up so often in discussions.
Their answer is that, surprisingly, it is a good heuristic. If you just bought something, you've shown intent, and there's a small probability that you're going to be unhappy with your initial purchase and back on the market soon, for whatever reason.
Is the posterior probability that you'll buy a mattress again shortly after buying one, a small chance? Yes, undoubtedly. But is it smaller than the prior probability of a random person who hasn't bought any mattress at all recently, suddenly being on the market for a mattress? Apparently not.
Weird things can happen with joint probabilities when both sides of the condition are very unlikely. Sort of like how your chance of having some rare cancer after testing positive on a ~99% accurate cancer screening test is still quite tiny.
[–] arjie 2 days ago
[–] driverdan 2 days ago
[–] autoexec 2 days ago
It probably helps when your marketplace is filled with knockoffs and misrepresented trash.
This makes no sense. If I just bought a mattress, the prior probability of a random person buying it is irrelevant. What's relevant is the posterior distribution of what people buy after buying a mattress. Yes, you might buy another one, but what about other bedroom accessories for example?
A mattress might not be the best example though, because what really matters here is the expected value from the posterior probability, which includes the price. So a more expensive product with a slightly lower probability of purchase might yield a higher expected value than cheaper products with higher probability.
Anyway, that's orthogonal to the discussion: my point is that it's the posterior distribution conditioned on the mattress purchase that should be modeled, not the comparison with random people that may or may not just have bought a mattress.