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If you stand back and think about how these schemes operate, you wouldn't actually _need_ ML to solve it.

Step 1 is getting a list of new products that are receiving a higher than expected number of 5-star reviews

Step 2, add it to a list of similar products.

Step 3, find accounts that all happen to purchase the same products in the same order.

Step 4, ban accounts and sellers.






"Step 1 is getting a list of new products that are receiving a higher than expected number of 5-star reviews"

What is higher than the expected number of 5-star reviews?

Don't those numbers vary widely depending on product category, brand, and newness of the product?

How does that work for things that fit into more than 1 product category?


> What is higher than the expected number of 5-star reviews?

That's left as an exercise for the reader ;)

But you could simplify this to new products, which if ungamed, would probably follow a predictable curve of discovery, as opposed to a games "instant 5 star" rating.


step 5: I now have a great way to boot competitors off of your marketplace.

That is a very simple algorithm! It’s also something the sort of people who do this will overcome relatively quickly. Automated or systemic attacks and mitigation is a war of escalation.

> If you stand back and think about how these schemes operate, you wouldn't actually _need_ ML to solve it.

As Machine Learning is just a fancy way of doing statistics, you will totally not need it for this.


You can do all this manually, but really you just described a form of machine learning.



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