[armanj]

Years ago, I often struggled to choose between Amazon products with high ratings from a few reviews and those with slightly lower ratings but a large volume of reviews. I used the Laplace Rule of Succession to code a browser extension to calculate Laplacian scores for products, helping to make better decisions by balancing high ratings with low review counts. https://greasyfork.org/en/scripts/443773-amazon-ranking-lapl...

[CuriouslyC]

Just for reference, in case you find yourself in an optimization under uncertainty situation again: The decision-theoretic right way to do this is generate a bayesian posterior over true ranking given ranking count and a prior on true rankings, add a loss function (it can just be the difference between the true rating of the selected item and the true rating of the non-selected item for simplicity) then choose your option to minimize the expected loss. This produces exactly the correct answer.

[yossarian22]

Can you please provide an example or link to read more? Seems very interesting.

[Shadowmist]

I always assume that all the ratings are fake when there is a low count of ratings since it is easy for the seller to place a bunch of game orders when they are starting out.

[anentropic]

A bigger problem I find is many Amazon listings having a large number of genuine positive reviews, but for a completely different product than the one currently for sale.

Recently I was buying a chromecast dongle thing and one of the listings had some kind of "Amazon recommends" badge on it, from the platform. It had hundreds of 5 start reviews, but if you read them they were all for a jar of guava jam from Mexico.

I'm baffled why Amazon permits and even seemingly endorses this kind of rating farming

[kragen]

While this is a good idea, I think it's unrelated to the Laplace transform except that they're named after the same dude?

[armanj]

I referenced 3B1B for the name: youtube.com/watch?v=8idr1WZ1A7Q