Here we go, the start of LLMs plugged into everything.
Do Netflix recommendations really need to use LLMs?
This post paints a very altruistic picture of how recommendations can be fed from user history, preferences, device and environment context etc. However they make no mention of needing to advertise content from paid clients, promote new releases, and increase views on certain underperforming content. I'm assuming that once their fancy LLM spits out some user recommendations, they are then run through another process to 'commercialise' the results before displaying them to the user.
Also, if "The Odyssey" is coming out and and you know I love Nolan, and Netflix is getting paid to promote it, would you rather them NOT recommend that to me?
These types of things are natrually constrained. If you send me too many ads, or bad content, I leave. So you are incentived to do a good job.
You could make the argument that you just need to be incrementally better than the other streaming service, but then you are giving away potential advantage and profit.
They don't pretend otherwise, the article clarifies that their LLM has to take into account "business constraints" and such (which gave me a pretty nasty impression as I read it).
I never understood why they don’t ask you what you like about a movie or tv show. I do not use streaming platform rating system because it’s so dumb: if I say I like “pacific rim” I get recommended all the dumbest sci-fi action contents I already know I will never watch. I love pacific rim for its esthetic, lighting, photography, and music. I couldn’t care less about mecha and generally hate modern sci-fi.
Like, just ask me instead of trying to guess incorrectly
Spotify seems to use an LLM for their new DJ feature, and it honestly is way better than any other music recommendation service I've ever tried. It will give you music that actually sounds like the music you ask for.
What I'm excited for is Bumble's AI matchmaker. LLM categorization may actually be the key to a decent dating app.
I just checked. The AI DJ showed me a suggested request button with the prompt "late afternoon reggae rock with Lorna Shore". That's like suggesting pickled fish with strawberry ice cream just because you happened to order both a few times.
Okay. Well so if the model will tend to converge on the median of a user’s interests as measured by engagement… the user would have to depart from the recommendations to increase the odds of being recommended something surprising, no?
It’s interesting to see how Netflix evaluates RecSys internally, inferring from how they are comparing GenRec to it. In both cases the premise seems to be that users mainly want to engage with more of the same.
I would be even more interested in some kind of comparison to Netflix’s much earlier system for exchanging reviews and recommendations among a user’s human social group. Seems like there could be some insights around the input of fresh signals from aligned but not strictly conforming participants.
> “Seems like there could be some insights around the input of fresh signals from aligned but not strictly conforming participants.”
Exactly. I think you nailed a good subreddit.
What is the raison d'être of the a subreddit if it’s not to share your emotional quest to “refill the vase” of your love affair with the object of shared affection?
For example, I’ve made my own, somewhat delightful, but also archaic side quests from O’Brian’s Aubrey-Maturin fiction series.
Gibbon’s “The History of the Decline and Fall of the Roman Empire”, interesting authorial voice combining fiction and historical figures, but the characters have only brief moments on the stage.
The excellent narrator Patrick Tull of the A-M series lead me to:
Childers, Erskine, “The Riddle of the Sands” (1903), the archetype of the modern spy novel—with sailing!
So you want to post your find to the community? Maybe you have retained a trace of the author’s voice? So you bring your new books to the subreddit in fellowship.
But woe to you if in your excitement to share your new find you inadvertently, or intentionally, or carelessly, or with too much subtlety, break the “catechism” or moral codes of your subReddit group and insult its saints.
Well, then you’re in trouble. But lest we forget, _fortune favors the bold_. (What are the A-M peeps going to do if I piss them off? SWAT my house?)
In this setting, they are essentially using it as a feature extractor.
As for moving to this versus your bespoke feature extractor, _given_ that your existing features and their compositions are not antithetical to their language representation, an LLM will be an equal or better feature extractor. But for example, considering the basic feature genre, if your collaborative filter has learnt features that smartly recommending a show with one text feature "sci-fi" to people whose preferences have the text feature "comedy" because of learned behaviour despite the text, then you have to verbalise this feature "scifi,laugh track" or maybe providing samples of the subtitles of the show or add a "frequently co-watched with" section (which contains comedy shows) to the prompt, to effectively get an LLM to do the same thing (or many other ways to induce a hybrid embedding)
In many cases, people have almost entirely verbalizable features and feature compositions in their existing systems even if it may not exactly be optimal. So it's a good idea to try out LLMs there.
Composition mentioned everywhere above is crucial. Provided you can verbalise your important features, LLMs can perform very strong deductions and compositions out of the box above and beyond our own feature interactions that we use with say xgboost setups. And it's dynamic in the sense that it gives you a foundation model you don't have to retrain to use new (verbalizable) interactions.
Netflix had an algorithm contest, when was it, like 20 years ago? A team made a highly effective content recommendation system and Netflix never used it. Why? It killed revenue.
Separately, there simply isn't enough content to recommend. It's not like you need help finding the perfect Wikipedia article. At any one time, there are ~50 things a user would probably watch.
Feels more like a soft pivot to cash in on AI valuations since Hollywood is cratering. Probably worth the investment.
Netflix has imo the worst recommendations on the entire internet. They are a Java shop that’s 5-10 years behind the industry in AI, and now they’re trying to use the most grossly inefficient and overcomplicated method out there to try to be trendy instead of just using a DLRM like everyone else was in 2020. What could go wrong?
Ok I was confused about this because LLM seems like an ill-fitting solution for a recommendation engine. This is much more classic machine learning stuff right
honestly, who cares. they could make the best recommendation engine in the world, but their catalog is abysmal. It's the Facebook of film/entertainment. My wife tends to watch some ai written slop on there as a guilty pleasure, but otherwise it's nonexistent to me and i don't bother opening up the app anymore
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[ 3.8 ms ] story [ 58.1 ms ] threadThis post paints a very altruistic picture of how recommendations can be fed from user history, preferences, device and environment context etc. However they make no mention of needing to advertise content from paid clients, promote new releases, and increase views on certain underperforming content. I'm assuming that once their fancy LLM spits out some user recommendations, they are then run through another process to 'commercialise' the results before displaying them to the user.
Also, if "The Odyssey" is coming out and and you know I love Nolan, and Netflix is getting paid to promote it, would you rather them NOT recommend that to me?
These types of things are natrually constrained. If you send me too many ads, or bad content, I leave. So you are incentived to do a good job.
You could make the argument that you just need to be incrementally better than the other streaming service, but then you are giving away potential advantage and profit.
Even though It’s probably exactly the type of thing I would do on a locally hosted LLM
Like, just ask me instead of trying to guess incorrectly
What I'm excited for is Bumble's AI matchmaker. LLM categorization may actually be the key to a decent dating app.
It’s interesting to see how Netflix evaluates RecSys internally, inferring from how they are comparing GenRec to it. In both cases the premise seems to be that users mainly want to engage with more of the same.
I would be even more interested in some kind of comparison to Netflix’s much earlier system for exchanging reviews and recommendations among a user’s human social group. Seems like there could be some insights around the input of fresh signals from aligned but not strictly conforming participants.
Exactly. I think you nailed a good subreddit.
What is the raison d'être of the a subreddit if it’s not to share your emotional quest to “refill the vase” of your love affair with the object of shared affection?
For example, I’ve made my own, somewhat delightful, but also archaic side quests from O’Brian’s Aubrey-Maturin fiction series.
Gibbon’s “The History of the Decline and Fall of the Roman Empire”, interesting authorial voice combining fiction and historical figures, but the characters have only brief moments on the stage.
The excellent narrator Patrick Tull of the A-M series lead me to:
Childers, Erskine, “The Riddle of the Sands” (1903), the archetype of the modern spy novel—with sailing!
So you want to post your find to the community? Maybe you have retained a trace of the author’s voice? So you bring your new books to the subreddit in fellowship.
But woe to you if in your excitement to share your new find you inadvertently, or intentionally, or carelessly, or with too much subtlety, break the “catechism” or moral codes of your subReddit group and insult its saints.
Well, then you’re in trouble. But lest we forget, _fortune favors the bold_. (What are the A-M peeps going to do if I piss them off? SWAT my house?)
https://www.reddit.com/r/AubreyMaturinSeries/
As for moving to this versus your bespoke feature extractor, _given_ that your existing features and their compositions are not antithetical to their language representation, an LLM will be an equal or better feature extractor. But for example, considering the basic feature genre, if your collaborative filter has learnt features that smartly recommending a show with one text feature "sci-fi" to people whose preferences have the text feature "comedy" because of learned behaviour despite the text, then you have to verbalise this feature "scifi,laugh track" or maybe providing samples of the subtitles of the show or add a "frequently co-watched with" section (which contains comedy shows) to the prompt, to effectively get an LLM to do the same thing (or many other ways to induce a hybrid embedding)
In many cases, people have almost entirely verbalizable features and feature compositions in their existing systems even if it may not exactly be optimal. So it's a good idea to try out LLMs there.
Composition mentioned everywhere above is crucial. Provided you can verbalise your important features, LLMs can perform very strong deductions and compositions out of the box above and beyond our own feature interactions that we use with say xgboost setups. And it's dynamic in the sense that it gives you a foundation model you don't have to retrain to use new (verbalizable) interactions.
Separately, there simply isn't enough content to recommend. It's not like you need help finding the perfect Wikipedia article. At any one time, there are ~50 things a user would probably watch.
Feels more like a soft pivot to cash in on AI valuations since Hollywood is cratering. Probably worth the investment.
Their recommendation system was seen as industry standard best in class.
I wouldnt potray them in negative light.
They are FANG, so they have some of the best engineers, and they need something to do.
Netflix also hit the problem of now having so many users that they can only grow by raising their prices.