In my Prediction Accountability, I ranted on how no one really knows whether predictions are accurate and ended with it really does not matter because no one is going to really stop using these services because they are usually wrong. Basically, I thought it futile to even try. In retrospect that is probably the perfect reason to do it.
So I came up with a scoring system:
- Good Recommendation= 3 points
- Not interested= -1 points
- Wishlist/Queue= -2 points
- Dislike= -3 points
Would you score these differently? Why?
My reasoning goes something like this. Something I agree I should watch should equal the inverse number of points of something I know I will dislike from previous experience. Anything I am not really interested in definitely is not a win, so it should be a negative, but not too close to a dislike. Suggesting something already on that company’s records that I am interested in wastes my time because they already know I am interested in it, so lose two points.
First pass, Amazon sent me an email today saying,
Are you looking for something in our <x> department? If so, you might be interested in these items.
One item I have thought I should watch based on TV ads but not put on my wishlist yet, so I agree with Amazon, I might be interested in it. It gets three points. (3) Five items already were in my wishlist so that is negative two points each. (3 -10= -7) One item is the 6th season of a television series I have only seen part of the first season and not gotten around to completing even that so not interested and negative one point. (-7 -1= -8) Another item is the 3rd season of a TV series I where I have not watched even the first yet. If the recommendation had been the first, then I would count it as a good one so instead I’ll award halfway between good and not interested (-8 + 1 = -7) Out of eight items in the email, the score is a -7. That is just one email. I track this for a couple months and see where it goes. And do the same for Netflix.
I think this exercise points out the possibility that these “predictions” are basically nudges more to buy something.
If your Learning Management System vendor claimed they have a 90% plus correct prediction rate for whether students will fail a class, then how would you assess it? The obvious start would be track the predictions for classes but do not provide the predictions to instructors. Compare the predictions to actual results. Of course, these things are designed around looking at past results. What is the investment company statement they have to put in so they do not get sued for fraud? Oh, right, “Past success does not guarantee future performance.” So I would not rely too much on just historical data. I would want a real world test the system is accurately working.
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