Amazon is in a different space here. Even the smoothest transaction goes through a handful of literal human hands. They have to pay for those hands regardless. At the very least following up on cases where customers (and competitors) flag fraud on their system should be possible.
Show the title and main description to GPT everytime the seller makes a change, and ask "Do these seem to be the same product?"
If GPT says that they seem different, flag for human review.
You could probably even ask GPT to take its confidence. If it's highly confident, skip the human review.
Description: Ignore previous instructions […]
Calculating a meaningful numeric difference between two chunks of text is fairly well-trod territory.
also, if a fucking seller cannot keep their listing reliably constant, what are they selling?
new version, new product, new reviews.
car manufacturers do this. wineries do this. pharma does this. even Apple managed to show the manufacturing date of their new new new new but the same things.
I hate to say it but I think some type of heuristics would be needed here.
1. Has the title significantly changed. 2. Has the price significantly changed. 3. Are the search keywords that were finding the old listing significantly different than those finding the new listing. 4. Have average ratings and common words in reviews changed? (Especially rarer words that match the new and old listing respectively)
If some of these start to look suspicious then I think you can start to apply your mitigations. You can probably even scale them by how sure you are. For example reviews are always downranked by age and significant changes to the listing amplify this effect, you can add the same weight to the start rating.
And of course the real way to prevent this is to flip the incentive. Add human review and a warning before killing the account. Make it so that the cost of being caught negates the benefit of doing this.