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Aligning Recommender Systems as Cause Area

I work at Netflix on the recommender. It's interesting to read this abstract article about something that's very concrete for me.

For example, the article asks, "The key question any model of the problem needs to answer is - why aren’t recommender systems already aligned."

Despite working on a recommender system, I genuinely don't know what this means. How does one go about measuring how much a recommender is aligned with user interests? Like, I guarantee 100% that people would rather have the recommendations given by Netflix and YouTube than a uniform random distribution. So in that basic sense, I think we are already aligned. It's really not obvious to me that Netflix and YouTube are doing anything wrong. I'm not really sure how to go about measuring alignment, and without a measurement, I don't know how to tell whether we're making progress toward fixing it.

My two cents.