

Great Recommendations Are Half Placebo - villesundberg
https://www.scoopinion.com/blog/great-recommendations-are-half-placebo

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nathan_long
A great recommendation engine should incorporate explicit feedback from users.

For example, Netflix knows what I've watched and how I've rated things. But it
doesn't let me tell it things like "I categorically dislike horror movies", so
it sometimes recommends those.

It's trying to guess something that I'd rather just tell it.

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eli
Isn't that what this does? <http://movies.netflix.com/TastePreferences>

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nathan_long
Oh, I never saw that! Thanks!

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pinko
Interestingly, I've found an opposite effect -- people tend to oversell their
favorite movies, restaurants, etc., so a person's strong recommendation will
often lead to disappointment, not because the movie/food isn't good but
because my expectations were set too high.

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villesundberg
This is a good point, and happens to me sometimes too in real life.

I've also found that some people read way too much into how well an automatic
recommendation system should work. There have been a couple of angry feedback
messages due to a guess being off.

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1rs
I think the 'black swan' is important in recommendations as well. There's
stuff that will blow your mind because it's something you think about and is
somehow connected to something you've done. It might not be relevant at all to
most people who read the same stuff as you do. So it's very unlikely it will
be recommended to you. Recommendations increase 'groupthink' and you find
interesting stuff but not a lot that's outside of your comfort zone. Humans
are still better at connecting dots and giving recommendations than machines.
I guess it's difficult to teach a machine to make great surprises?

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shokwave
The author writes as if, when a 'real person' makes a recommendation, they
aren't just making a prediction from data. The difference is that we can see
how the algorithm works, whereas our picture of what the human brain does when
it thinks about what someone might like is very murky.

And even if trusting a recommendation causes a placebo effect that increases
the perceived quality (as others have pointed out, it seems plausible that a
hyped recommendation may cause disappointment instead), why on Earth would we
trust a process we _don't_ understand, but mistrust a process we _do_?

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obviouslygreen
_At the same time, it's hard for me to trust algorithmic recommendations. I
don't trust them because I know they're from a computer, and I find it
difficult to believe that a computer program can "know" who I am._

Determining something that's likely to suit your tastes based on your
browsing/buying/watching/reading/etc. habits isn't "knowing" you and doesn't
require such a thing. I don't know if this is some sort of accidental
anthropomorphism or what, but I've seen this in more than one place and it
just doesn't follow.

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eurleif
The whole point was that there's an emotional component to how good
recommendations seem.

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calhoun137
This article made me laugh, let's review.

Computer predictions seems to work, but only get you half way there. I don't
trust them because they are from a computer, but if I wrote the program myself
I would trust it 100 times less. As with baseball, guessing the home team will
win works at least half the time. Our recommendation engine works, and people
read articles we recommend 4 times longer.

Conclusion: computer based recommendation engines are 50% placebo.

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gnosis
The only thing that ultimately matters to me as far as recommendators (whether
human or algorithmic) are concerned is whether their recommendations turn out
to be valuable to me in some way.

That's how you build trust: by making good recommendations. Everything else is
secondary.

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patdennis
> All additional information will make your prediction better

I don't think this is true... I find that there's a point where additional
information becomes noise, and can make your prediction less useful.

