
Categories of deep recommendation systems - le_james94
https://jameskle.com/writes/rec-sys-part-2
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fjabre
The recommendation systems (i.e. Netflix) for movies and music are still just
awful in terms of guessing my tastes.

I still find that human based recommendations for things like music and movies
consistently beat the algorithm at predicting what I'll like vs what I won't
like just about every time.

IMHO it will be many years before these recommendation engines consistently
pick media I like to watch or listen to.

They are still far inferior to human based recommendations.

Guessing what someone will want to watch at any given moment is probably a
harder problem than getting a car to drive autonomously.

EDIT (source): I work on a mostly human powered recommendation engine/expert
system @ lazyday.tv. It does not use ML to make recommendations. It takes some
creative approaches to solving the movie recommendation problem using an
expert system and optimizing search results.

~~~
jcims
Is there any evidence that the recommendation systems are actually intent on
putting something you would like to see in front of you instead of some other
incentive?

My YouTube subscriptions are all
engineering/programming/space/guns/machining/electronics. I even see the same
commenters in many of them. There is clearly a set of people with these
interests that could be used to cross-pollinate interesting content. Yet my
recommendations are all pop culture and sports.

Spotify is the same way but i blame my kids for polluting their model of my
interests,

I’ve given up on Netflix. I can’t even find stuff i want to watch on there any
more.

~~~
catalogia
> _Is there any evidence that the recommendation systems are actually intent
> on putting something you would like to see in front of you instead of some
> other incentive?_

Something I've been wondering about recently is how infrastructure costs
factor into this. Obviously videos get served from a local CDN node when
possible, but I'm guessing sometimes the file you're requesting is uncommon
enough that the local CDN node doesn't have it. Maybe this isn't the case with
Netflix, but it's probably the case with youtube, just because they have that
much more content. Adding more storage to the CDNs would cost money, and cache
misses on the CDN also cost money, so does youtube have a financial incentive
to bias their recommendations towards videos already cached near you?

~~~
hooande
the cost of cache misses, even at google scale, is still trivial compared to
their revenue

~~~
jcims
The infrastructure costs might be low, but transit performance back to Google
is terrible for many ISPs (looking at you CenturyLink), so there might be
'quality' costs to consider as well.

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timkam
There are recent works that question whether "deep" learning algorithms, and
in particular neural networks, are really advancing the state of the art of
recommender systems:
[https://arxiv.org/pdf/1907.06902.pdf](https://arxiv.org/pdf/1907.06902.pdf)
(Best Paper Award, RecSys 2019). It is strange that the blog post fails to
mention this.

~~~
amznthrowaway5
Yeah, this has been a well known problem in the field for a while now, and not
just with DNN approaches
[https://en.wikipedia.org/wiki/Recommender_system#Reproducibi...](https://en.wikipedia.org/wiki/Recommender_system#Reproducibility_crisis)

These systems almost never work in production. The publications are usually
very deceptive.

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runnerup
I recently found a particularly rare/esoteric song on Google Play Music,
specifically because I wanted to hear more like it. The song was "Light" by
Idyllic.

I clicked "Start radio" and the first song was "light", of course. Then all
the next 10 songs were by Idyllic only, no other artists. After that half the
songs were Idyllic, the other half were....songs from my "Thumbs Up" playlist
(any song I had previously liked).

I repeated the process several times thinking there was a bug mixing in my
thumbs-up playlist.

Eventually I realized I'm the first person to ever listen to this song on
Google Play Music and it used only me as the first bayesian filter - so I just
got my own music suggested back to me.

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jayparth
IMO this article does not deliver on its title. I was expecting a list of
systems that we should "pay attention to"\- that hold promise in the coming
years or now.

Instead, this was an index of current literature from pretty much all deep
learning methods that have been applied to recommender systems. It's more
useful to a lay person than a researcher.

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everling
Many film recommendation systems suffer from a strong gravitational pull
towards mainstream choices with no clear association to the input (”people who
enjoyed The Thin Red Line also enjoyed Pulp Fiction..”). But I wonder what
better results deep learning could even accomplish given a limited feature
space such as user like/dislikes.

A few years ago I made a recommender based on critics’ inferences in reviews.
For the right kind of film (indie or festival circuit stuff and the like) it
yielded quite interesting results.

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sidpatil
The article's original title is much more readable than this post's title.

~~~
ajflores1604
> The article's original title is much more readable than this post's title.

Probably too long to fit, op did a decent job of compressing imo.

The original is:

RECOMMENDATION SYSTEM SERIES PART 2: THE 10 CATEGORIES OF DEEP RECOMMENDATION
SYSTEMS THAT ACADEMIC RESEARCHERS SHOULD PAY ATTENTION TO

~~~
kevinwang
The “that” being left out makes it tough to parse

~~~
15__characters
The "to" missing at the end totally breaks it for me, I read it as "
_Categories of Deep Recommendation Systems: Researchers Should Pay Attention_
"

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zitterbewegung
Read the title and thought it was going to be a Paper about how to use
Attention based neural networks for deep recommendation systems...

