A Bayesian view of Amazon resellers (2011)

When I started learning about Bayesian statistics years ago, I was fascinated by the idea that a statistical procedure might take some data in a form like "94% positive out of 85,193 reviews, 98% positive out of 20,785 reviews, 99% positive out of 840 reviews" and give you an objective estimate of who is a more reliable seller. Unfortunately, over time, it become clear that a magic bullet does not exist, and in order for it to give you some estimate of who is a better seller, YOU have to provide it with a rule for how to discount positive reviews based on their count (in a form of a prior). And if you try to cheat by encoding "I don't really have a good idea of know how important the number of reviews is", the statistical procedure will (unsurprisingly) respond with "in that case, I don't know really how to re-rank them" :(

If anyone wants to get more into Bayesian stats, I will always recommend Statistical Rethinking by Richard McElreath. Maybe my all time favorite text book. He has an accompanying youtube lecture series as well.

Is there an adaptation of Bayesian statistics that also takes into account timeliness of the data ? e.g. a more recent string of negative reviews would potentially indicate something compared to a more smooth distribution

(2011) The old days when Amazon reviews carried information.

See also <a href="https:&#x2F;&#x2F;www.evanmiller.org&#x2F;how-not-to-sort-by-average-rating.html" rel="nofollow noreferrer">https:&#x2F;&#x2F;www.evanmiller.org&#x2F;how-not-to-sort-by-average-rating...</a>

Can someone tell me the answer to the problem in plain english? I have no idea what a beta is.

hmm... I might start discounting sellers with too many reviews. Not sure where the cutoff would be and some sellers might actually be high volume and get lots of reviews, but a huge number of reviews makes me think they are fake.

I think Amazon used to use the lower bound of a CI to sort? Or it used to be an option, then some sellers sued or threatened to based on the argument that it discriminated against smaller sellers?

A lot of words to describe bayes rule

needs 2011