
Learning Tree-Based Deep Model for Recommender Systems - Matfyzak
https://arxiv.org/abs/1801.02294
======
joe_the_user
Recommendation systems are a deep learning perennial since the Netflix
contest.

My impression of Youtube, Amazon, Tumblr and other is that the recommendation
process in practice is close to useless. And this isn't because I don't want
recommendations.

Moreover, in all these situations, I feel like it just be improved by _asking_
the user instead. There are thing I'd love recommendations on and I'd be quite
willing to _tell_ these portals about my preferences. But it doesn't seem like
they want to know, don't have internal search worth much, don't take my search
terms account when recommending, etc.

It seems like in practice, most sites actually want the effects of not being
able to immediate drill past items that leverage their crappy interface to get
exposure since I assume there are extra profits in one form or another to be
made with these.

~~~
sadikkapadia1
I wrote the recommender at Netflix about 5 years ago (every line of code).
Netflix has been degrading it since then. The problem is that many companies
are hotbeds of politics over expertise. Recommender, UI, A/B tests, etc are an
excellent venue for politics at the expense of the product.

Some anecdotes from Netflix:
[https://www.reddit.com/r/MachineLearning/comments/6xiwr4/d_w...](https://www.reddit.com/r/MachineLearning/comments/6xiwr4/d_why_netflix_never_implemented_the_algorithm/)
(Scroll down for my comments. Ignore namp243 comments - see below).

Another example is the "Netflix prize team" at Verizon/Yahoo. They refuse to
share data with any other groups (they are afraid of being discovered),
leaving other groups literally nothing to do.

In my opinion Youtube recommendations are improving. They are still pretty bad
but they are trying interesting approaches.

~~~
codesuki
Can you share any ideas on how you would implement a new recommender these
days? Any papers you like? I am trying to learn more about this topic, but
good information is hard to come by. The best resources I found so far are
presentations from Netflix and Spotify, but they skip over so many details and
assume so much knowledge that it's hard to get good results without being able
to consult someone with experience.

~~~
sadikkapadia1
[https://research.google.com/pubs/pub45530.html](https://research.google.com/pubs/pub45530.html)
is the most complete recent paper I've seen.

~~~
codesuki
Thanks! So, would you say the future for recommendation is deep learning?
While I am not opposed to it, I find it very opaque.

~~~
sadikkapadia1
The future is a long time. Eventually faster computation, larger memory would
allow taking smaller and smaller steps during training (coupled with avoiding
"bad optima" with stochastic training). All of this would improve robustness
of training.

The domain dictates whether degree of opacity (or other attributes), would
rule out deep learning.

Netflix recommender does not use deep learning (which is pretty amazing given
how badly they have messed it up). From the conversations I had (a couple of
years ago), they gave up with it. I'm sure the Youtube team could do a better
job on the Netflix data then they managed to do.

------
1000units
The first sentence of the abstract is poor English, and the fifth contains a
factual inaccuracy about a very well-known recommendation algorithm
(collaborative filtering is not content-based).

~~~
thesz
I agree. The fine article is very poorly written.

I was not able to find out whether they used decision trees or something
novel.

