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Real World Recommendation System

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Re: Real World Recommendation System

#71

Post by invalidOrTaken »

chuckcode wrote: post #21
fmakunbound wrote: post #5

With all of this technology applied, I am still disappointed by Netflix's recommendations – to the point of just giving up and doing something else.

Do you think part of this is that Netflix has assumed zero effort from user model? My experience has been that Netflix does an ok job of recommendations, but fails at overall discovery experience. There is no way for me to drive or view content from different angles easily. I end up googling for expert opinions or hitting up rotten tomatoes to get better reviews. Netflix knows a ton about me and their content, but se…

>Do you think part of this is that Netflix has assumed zero effort from user model?

Talking w/a friend who works at Netflix, it sounds like this is a warranted assumption. The way he told it, they were tearing their hair out at one point b/c users wouldn't put much into it.

Re: Real World Recommendation System

#72

Post by aczerepinski »

tempest_ wrote: post #57
oofbey wrote: post #51

Off-topic, but how did Netflix manage to get itself inserted into the FAANG acronym anyway? Their impact on the tech industry is trivial compared to all the others. Sure, if you just take out the N it's offensive, but we could have said "GAFA" or "FAAMG" would be more accurate to include Microsoft in their place.

All the cool kids say GAMMA now.

What is the G?

Re: Real World Recommendation System

#73

Post by notafraudster »

imilk wrote: post #46
notafraudster wrote: post #35

Earlier quoted context omitted.

It's a short-hand for the treatment of the subject being pretty shallow and non-descript, which seems to apply to this article exactly. I read this and didn't learn anything.

Saying the article is "pretty shallow and non-descript: is much shorter and more useful than what they posted.

Right, but then it starts a meta-conversation about why the article got posted, or even written. It doesn't have the down-the-rabbit hole trait of an individual project of passion, or the sort of authoritative voice of a conference talk or even a Netflix blog post, it doesn't really speak to specific actionable technologies so it's not the kind of onboarding a Toward Data Science post would be. And that meta conversation inevitably leads to, oh, it's a marketing funnel. So just saying "this is content marketing" I think is a shibboleth for the entire conversation that starts with "pretty shallow and non-descript".

Of course I didn't write the original comment and there's something to say for flag-and-move-on or whatever, and other people did enjoy it. I'm just saying I understand the impulse to short-circuit the entire tedious conversation!

Re: Real World Recommendation System

#74

Post by jedberg »

oofbey wrote: post #68
jedberg wrote: post #56

Earlier quoted context omitted.

FAANG was created by the TV personality Jim Cramer to talk about high growth tech stocks. At the time Netflix was doubling every year. It was based purely on finance. It's now been taken over by the tech industry to be shorthand for places that are highly selective in their hiring and tend to work on cutting edge tech at scale. That being said, the impact of Netflix on tech is pretty big. They pioneered using the clo…

> They pioneered using the cloud to run at massive scale. Which is to say they were AWS's biggest early customer? Doesn't really seem like Netflix should get the credit for that one.

It was a lot more than that. They developed systems and techniques that even Amazon adopted and are still adopting to this day. They also created a ton of open source tools for other people to use the cloud:

https://netflix.github.io

Netflix tech even spawned a company to sell their open source tools:

https://www.armory.io

And they codified the entire practice of Chaos Engineering:

https://en.wikipedia.org/wiki/Chaos_engineering

Re: Real World Recommendation System

#75

Post by [deleted] »

oofbey wrote: post #68
jedberg wrote: post #56

Earlier quoted context omitted.

FAANG was created by the TV personality Jim Cramer to talk about high growth tech stocks. At the time Netflix was doubling every year. It was based purely on finance. It's now been taken over by the tech industry to be shorthand for places that are highly selective in their hiring and tend to work on cutting edge tech at scale. That being said, the impact of Netflix on tech is pretty big. They pioneered using the clo…

> They pioneered using the cloud to run at massive scale. Which is to say they were AWS's biggest early customer? Doesn't really seem like Netflix should get the credit for that one.

[deleted]

Re: Real World Recommendation System

#76

Post by whimsicalism »

KaiserPro wrote: post #17

> As a result, primary databases (e.g. MySQL, Mongo etc.) almost never work I mean it does. As far as I'm aware Facebook's ad platform is mostly backed by hundreds of thousands of Mysql instances. But more importantly this post really doesn't describe issues of scale. Sure it has the stages of recommendation, that might or might not be correct, but it doesn't describe how all of those processes are scheduled, coordin…

I disagree - this seems quite clearly to address issues of scale, going into multiple-pass ranking, etc. etc.

Re: Real World Recommendation System

#77

Post by notriddle »

chudi wrote: post #47
endisneigh wrote: post #20

Is there any recommendation system people we actually happy with? They all seem to suck in my experience

all feeds are recommendations systems, instagram, facebook, twitter, tiktok, youtube, every single one is a recommendation system.

Technically, yes, but when they're talking about this sort of thing, they mean "personal recommendation system" or "content-based recommendation system."

For example, the HN front page is a recommendation system if you literally mean system-that-recommends-web-pages-to-look-at. But it's not personalized; every visitor sees the same front page. This fundamentally makes it a different sort of thing.

Re: Real World Recommendation System

#78

Post by whimsicalism »

lmc wrote: post #40
hallqv wrote: post #22

Anyone have recommendations (no pun) for more in depth resources on the subject (large scale recommendation systems)?

Much of the field seems to be fixated on throwing massive compute resources at models with results that can neither be evaluated nor reproduced. "the Recommender Systems research community is facing a crisis where a significant number of papers present results that contribute little to collective knowledge […] often because the research lacks the […] evaluation to be properly judged and, hence, to provide meaningful…

By "the field", you surely mean the academic field. In the industry, we run controlled experiments to validate all the time.

Recommender systems is one of the few areas in ML where almost all of the knowledge is contained in industry, not academia.

Re: Real World Recommendation System

#79

Post by hbn »

vincentmarle wrote: post #53
oofbey wrote: post #51

Off-topic, but how did Netflix manage to get itself inserted into the FAANG acronym anyway? Their impact on the tech industry is trivial compared to all the others. Sure, if you just take out the N it's offensive, but we could have said "GAFA" or "FAAMG" would be more accurate to include Microsoft in their place.

There was a point in time when FAANG offered the best compensation packages for engineers (Netflix was one of them) - so that's where the term originated from but while it's outdated in many respects (Microsoft is not included, Facebook is now Meta, Google is now Alphabet etc etc) it's still sticky for some reason.

> Facebook is now Meta, Google is now Alphabet

Eh, the new parent company names aren't really what people know them as still. I don't think most people are even aware that Google has a parent company.

I have a friend that works at Google, and that's what we say. I don't think him or anyone would ever say he works at Alphabet.

Re: Real World Recommendation System

#80

Post by HWR_14 »

samstave wrote: post #33
vikingcaffiene wrote: post #3

Gentle reminder to anyone reading this that your problems are probably not FAANG problems. If you architect your system trying to solve problems you don't have, you are gonna have a bad time.

Wow, this is something that has been a floater-in-mind for decades ; I'll top it off with an interview at Twitter with the Eng MGR ~2009-ish? -- Him: So tell me how you would do things differnetly here at twitter based n your experience? ME: " Well, I have no idea what your internal processes are, or architecture, or problems, so my previous experience wouldn't be relevant. " I'd go for the best option that suits goa…

Wow. That amazes me that anyone would answer that question without knowing anything about the problem space and implemented solutions.

Wait, I got it, I would rewrite everything as AWS Lambdas. That's the right answer! Screw your (almost certainly SQL) DB, let's move it all to DynamoDB too.

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