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Instagram’s Explore Recommender System

instagram-engineering.com

41–50 of 59 posts

Re: Instagram’s Explore Recommender System

#41

Is this really AI? This seems like simple classification and ranking. Honestly I didn't see anything new in there that hasn't been around for the past 10 years. KNN? NDCG? That's entry level ML. TFA does throw around neural networks a bit, but doesn't go into any detail. EDIT: Maybe I'm just thrown off by the "Powered by AI" part of the article title. I was expecting more I suppose.

the concepts are simple but doing it at scale is hard. there are some good insights on scaling here:

* scaling to more engineers/products: IGQL is an interesting way to compose ML pipelines with straightforward syntax

* scaling to more ranking candidates: an active user with a large follow graph who loads the explore tab likely has millions of eligible candidates - how do you load those fast? the idea of using a "distilled" model as a first, light ranking before using a full model as the final predictor is a good intuitive idea that I haven't seen described before.

* scaling KNN is hard: FB has done interesting work to make approximate nearest neighbor search fast, and opensourced it (the FAISS library which is referred to in the post). the improvements here are certainly non-trivial.

* scaling to more users: creating useful general purpose user embeddings is hard!

* scaling to more objectives: instagram has many business objectives, e.g. likes, follows, minimizing hides, so there is a need to have multiple models making many predictions. There is also a need to weight them intelligently, which is where the Bayesian optimization libraries come in.

in some sense, nothing is truly AI, but this is useful work which you can learn a lot from.

Re: Instagram’s Explore Recommender System

#42

If instagram's product KPIs were more inline with what I wanted as a user, this would be great...but they're not and my explore feed is frequently filled with models, child musical prodigies, and other popcorn-esque content. Compared that to Spotify, whose goal I presume is to get me to listen to more music and buy tickets and merch through their occasional marketing. I'm a music snob but damn does Spotify get me gre…

What sort of KPIs would you prefer? I work on similar things at IG and curious what you would suggest.

Re: Instagram’s Explore Recommender System

#44

Is this really AI? This seems like simple classification and ranking. Honestly I didn't see anything new in there that hasn't been around for the past 10 years. KNN? NDCG? That's entry level ML. TFA does throw around neural networks a bit, but doesn't go into any detail. EDIT: Maybe I'm just thrown off by the "Powered by AI" part of the article title. I was expecting more I suppose.

True. It sets rightfully under the engineering category, because all this does is just engineering. At scale is challenging, but with Facebook's resources, it is more like problem waiting to be tackled, but the stakes are not high, because it CAN be tackled, just how much resources you want to pour into.

What makes this mildly interesting is the IGQL, but again, without knowing the full syntax, it feels pretty restrictive.

Youtube is doing much more advanced stuff, like Reinforcement learning@Scale, as comparing to Instagram in this regards.

Re: Instagram’s Explore Recommender System

#45

Is this really AI? This seems like simple classification and ranking. Honestly I didn't see anything new in there that hasn't been around for the past 10 years. KNN? NDCG? That's entry level ML. TFA does throw around neural networks a bit, but doesn't go into any detail. EDIT: Maybe I'm just thrown off by the "Powered by AI" part of the article title. I was expecting more I suppose.

Embeddings have only really been popularized in the last few years. The 'original' word2vec paper was 2013. It's really only been maybe two years that using embeddings on other things has been popularized.

http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf

Bengio has a paper in 2003 that describes almost the same idea as word2vec (CBOW model to be exact).

Re: Instagram’s Explore Recommender System

#46
post #5

It can go pretty wrong - look at a picture of a gun one time and IG will start shoving gun pictures down your throat, it's horribly non-forgiving and can't be tuned by the end user easily. You learn pretty quick to never look even once at something you don't want a whole lot of that same thing force-fed to you - on the other hand, start looking at huskies and you'll get tons of puppers filling your explore. :)

Instagram's Explore page is encouraging narrowcasting, which is arguably very detrimental to society/users in general.

Re: Instagram’s Explore Recommender System

#47
post #34

If instagram's product KPIs were more inline with what I wanted as a user, this would be great...but they're not and my explore feed is frequently filled with models, child musical prodigies, and other popcorn-esque content. Compared that to Spotify, whose goal I presume is to get me to listen to more music and buy tickets and merch through their occasional marketing. I'm a music snob but damn does Spotify get me gre…

I've had terrible luck the past couple years with Spotify's Discover Weekly. Last time I remember it being good was Fall 2016. Now my "Discover" Weekly playlist has me "discovering" the same exact songs over and over. I've been pigeonholed way beyond what I thought possible. Do other users really engage with the same 10 songs over and over and over that this is the default behavior of their recommendation engine? I g…

Yeah I'm getting this problem as well.

I need a better diverse and robust recommendation system from Spotify (at this point I'm addicted, I listen maybe 5 hours on average) and a lot of times I get the same n number of songs again and again.

99% of the weeks Discover Weekly has come out, they have 1 or 2 real nice songs but the rest are the same "garbage" I've been listening to for a while.

Anyways, I agree, Discover Weekly needs a revamp.

Re: Instagram’s Explore Recommender System

#48
post #5

It can go pretty wrong - look at a picture of a gun one time and IG will start shoving gun pictures down your throat, it's horribly non-forgiving and can't be tuned by the end user easily. You learn pretty quick to never look even once at something you don't want a whole lot of that same thing force-fed to you - on the other hand, start looking at huskies and you'll get tons of puppers filling your explore. :)

Same thing happens with youtube, at least in my experience. The time i click by mistake on a video that really doesn't interests me i feel like i'm doomed.

Re: Instagram’s Explore Recommender System

#49
post #37
post #34

Earlier quoted context omitted.

I've had terrible luck the past couple years with Spotify's Discover Weekly. Last time I remember it being good was Fall 2016. Now my "Discover" Weekly playlist has me "discovering" the same exact songs over and over. I've been pigeonholed way beyond what I thought possible. Do other users really engage with the same 10 songs over and over and over that this is the default behavior of their recommendation engine? I g…

i have a separate problem. i got a puppy and there was a time where i had to just put in headphones while i was crate training him, and listen to rain sounds to fall asleep, instead of his cries. months later, all i get on my discover feed are a bunch of mellow rain/sleepy songs. I dont actually listen to that crap, i just want it periodically for certain things. there needs to be a way to tag songs as "DO NOT CONSID…

Similar situation here, I use spotify for music during D&D games (you can find some amazing playlists on reddit / dnd forums), but now 20% of what's in my mixes is generic fantasy and halloween background ambiance.

Re: Instagram’s Explore Recommender System

#50
post #5

It can go pretty wrong - look at a picture of a gun one time and IG will start shoving gun pictures down your throat, it's horribly non-forgiving and can't be tuned by the end user easily. You learn pretty quick to never look even once at something you don't want a whole lot of that same thing force-fed to you - on the other hand, start looking at huskies and you'll get tons of puppers filling your explore. :)

Same thing happens with youtube, at least in my experience. The time i click by mistake on a video that really doesn't interests me i feel like i'm doomed.

Youtube seems heavily biased towards things I’ve viewed most recently on the homepage. I actually like Youtube’s recommendations more than any other website, especially the similar videos section on video pages themselves. It seems to recommend genuinely related videos and usually ones with a certain base level of ‘quality’. It’s good at things like picking out dates in the video title and listing other videos for that date/event.
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