
If You Liked This, You're Sure to Love That (2008) - mpiedrav
https://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html
======
0xcde4c3db
Just based on the experiences I have with them, I feel like the Big Problem
with recommendation engines is that they're bad at teasing out _why_ somebody
liked something and end up falling back on an obfuscated equivalent of P(likes
A | likes B), which ends up dominated by people obsessed with a particular
genre, franchise, or theme. Or worse, it relies on extremely generic tags. If
you buy, say, Steins;Gate and Rosenkreuzstilette on Steam, it's going to keep
recommending the most garbage VNs and PG-13 nukige on the planet until the sun
explodes because you obviously like VNs and anime (as opposed to quirky sci-fi
stories and Mega Man).

There's a paragraph about this in the article that discusses automated
identification of clusters, but it makes it sound like there was a lot of
human judgment/guesswork in figuring out _why_ any given set of movies
clusters the way it does.

> Each new algorithm takes on average three or four hours to churn through the
> data on the family’s “quad core” Gateway computer.

Huh. I don't think I would have guessed that Gateway was still around in the
days of quad-core desktops.

~~~
darepublic
What we need is to monitor people's brains in some way while they are watching
entertainment to see specifically what gets evoked and when. I would do it if
I had the means. Then we can create original stories again, and run them
through our models of human amusement and catharsis to know who will enjoy
what and why and when.

~~~
sverige
No need for all that. There are no original stories, only variations on the
ancient ones. Here's one summary:

[https://en.m.wikipedia.org/wiki/The_Thirty-
Six_Dramatic_Situ...](https://en.m.wikipedia.org/wiki/The_Thirty-
Six_Dramatic_Situations)

As for who will enjoy what etc., there are entire industries that spend their
efforts on knowing that, like Nielsen and Rotten Tomatoes.

~~~
danShumway
These are extremely broad categories. It's a bit like saying, "there are no
original books, they're all just collections of the same 26 letters", or "all
stories are the same, they're just characters doing things."

One category listed here is Pursuit:

> the fugitive flees punishment for a misunderstood conflict. Example: Les
> Misérables

By this logic, 'Les Miserables', 'Mission Impossible', and 'Catch Me if You
Can' are all just rehashing the same basic idea. On an existential level,
sure, but is there any practical use to a category that broad?

------
Balanceinfinity
Seems like any one data point would be worthless. If I like "Sleepless in
Seattle" (which I didn't) it might be because of Romcom (no) or because of
Ryan (no) or because of Hanks (yes). I wonder if there's a cluster of 10
movies, which if you knew the answer about all 10, it would predict how you
would feel about a specific #11.

~~~
hairofadog
This! This is what drives me bananas as an Apple Music user: you can create
radio stations based on a single album, song, or artist, but not on a
playlist. I have come to think of it as the "Vince Guaraldi" problem, which is
that if you tell Apple Music to create a radio station based on Vince Guaraldi
it'll very quickly veer off into soundtracks from children's films (because of
the Peanuts soundtracks) rather than what I want, which is piano jazz trios.
This could also be because Apple relies on metadata (soundtracks, family
entertainment) more than the attributes of the music (which instruments are
being played, tempo) but it feels like a problem that could be solved by
allowing radio stations based on playlists and it boggles my mind when each
new release of Apple Music doesn't include that feature.

~~~
parliament32
Google Play has this feature and it's great, I'll often put a group of songs I
like into a playlist and start radio from it. Best way to "discover" new music
I might like, and far better than their curated radio stations.

------
greggyb
I was involved with design and planning for a solution to predict unscheduled
maintenance and warranty claims for vehicles. I think that we had a pretty
similar problem. Unfortunately I did not stay with the company I was working
for, so I can't speak to the effectiveness of the approach. A simplified
example is below.

There is tons of scheduled maintenance. And there are also lots of common
unscheduled maintenance items. These were basically worthless in any naive
prediction. Basically "Ah, I see that an engine failure is always preceded by
dozens of oil changes." There's a tiny predictive value, because number of oil
changes is a proxy for age/use; older engines are more likely to fail.

But that sort of prediction wasn't helpful. We ended up proposing a strategy
that I shorthand to "without which, not". Basically we wanted to classify the
events which precede a specific type of failure, but which we don't see in
similar vehicles without the failure. This is grossly oversimplified.

An analog would be the idea of necessary vs sufficient pre-conditions. We were
trying to identify the sufficient pre-conditions.

To draw the analogy to a recommendation engine, a common failure mode is that
popular things get recommended a lot. Another failure mode is that they
basically become genre filters. This is discussed elsewhere.

So in a movie context, we wouldn't be looking for "what do other people who
like this movie watch"? Because that's a genre popularity contest. We would
ask "Given that a user likes Movie A, what did they watch before that other
Movie A likers didn't watch?" And then look for clusters there.

Again, I'm grossly oversimplifying this, because I didn't get to implement the
solution I mentioned. The approach seemed promising, though. You might
consider this a nuance on "down-weighting popular things", but it's not quite
that.

------
alexpotato
If I remember correctly, there was a winner for this but then Netflix just
ended up scrapping the whole contest/project and creating a new recommendation
engine.

~~~
michaelcampbell
They paid the winners and didn't use the entry I think. Which is a shame,
because their engine BEFORE the "winning" one, which was presumably better,
was great. Whatever they're using now is subjectively much worse.

Could be I'm misremembering, but if I am I think I'm in good company.

~~~
SketchySeaBeast
Do they actually use an engine now, or just throw up a random mish-mash of
Netflix originals all over the screen? Most of them are in genres I've never
watched with actors I've never expressed interest in, and yet there they are.
"Netflix Original" is not a category that you should be using to link other
movies to me Netflix.

~~~
cpeterso
I heard a third-hand rumor that when Netflix switched from 1-5 star ratings to
thumbs up/down, they stopped even using your ratings to personalize
recommendations. They were simply using watch time. I've since read someone
else on HN say that is incorrect. There are old films I really like but have
not streamed on Netflix. If I give them a thumbs up, it would be a pity of
Netflix didn't take incorporate that data.

------
SamBam
A big question is how much these recommendation engines have led to us
surrounding ourselves with the "same old" stuff, from music to political
opinions.

