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To measure its effectiveness, the standard approach is to run a live experiment and measure the change in metrics like revenue or CTR.
But imagine we suddenly replace all of Amazon's book suggestions with pornography. Or suppose that we replace all of Amazon's related books with shinier, more expensive items.
So to improve their recommendations, Amazon could try improving its topic models, add age-based features to its books, distinguish between textbooks and novels, and invest in series detectors.
(Of course, for all I know, they do all this already.) We now have a general grasp of Amazon's related book suggestions and how they could be improved, and just like we could quote a metric like a CTR of 6.2% or whatnot, we can also now quote a of 0.62 (or whatever).
While they might increase clicks and views initially, they're probably not optimizing user happiness or site quality for the future.
So how can we avoid them, and ensure that the quality of our suggestions remains consistently high?
Update Star has been tested to meet all of the technical requirements to be compatible with Windows 10, 8.1, Windows 8, Windows 7, Windows Vista, Windows Server 2003, 2008, and Windows XP, 32 bit and 64 bit editions.
In short, , a critics-loved-it book about cancer and romance (and now also a movie).
Two of the suggestions are completely random: a poorly-rated Excel manual and a poorly-reviewed textbook on sexual health.
The others are completely unrelated cowboy books, by a different John Green.
Here's the page for judgments paradigm, in which judges rate how relevant a book is to the original on an absolute scale.