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
Compared to modern social media platforms they are eons behind ofc social media has more content and more data ofc.
But their BigData and stuff was bleeding edge in pre-2018 in 2026 they really are 5-8 years behind they have not progressed much from what I can tell in both my usage experience, and their technical outputs over the years.
And yes that's the thing about modern development you fall behind really quickly.
Think about it Google was the company to create modern LLMs but people now dog on their LLMs. Apparently even SpaceX is ahead (I don't exactly know but maybe it is).
Being ahead once doesn't mean you are ahead today.
Netflix excels at maximizing engagement. The idea that you should A/B thumbnails for individual users, for example.
All the world-class data science doesn’t change the fact that the catalog doesn’t actually have many “hidden gem” titles. I find it somewhat hypocritical to argue that naive recommendations skew to the mean, when Netflix will happily pull funding for quirky shows that underperform in front of a general audience.
a nation can emerge as a superpower with advanced infrastructure at one point in time, and still have antiquated infrastructure in the present, no high speed rail and overly complex solutions to make up for it
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.
> A team made a highly effective content recommendation system and Netflix never used it. Why? It killed revenue.
Nope. This is an internet urban legend. The Netflix prize was only ever for marketing and recruitment. They never intended to deploy whatever winning weights that win because they overfitted on the training and validation data. The dataset they released was not their actual full dataset. It contained zero personal information, just (userid, movieid, rating, date) which is only ever going to get you so far compared to what they do internally.
And it wasn't "a team" that beat the baseline of a ranking algorithm Netflix never deployed, dozens of teams were within fractions of a point from each other. Hence the over fitting strategy.
I think the biggest change from the Netflix prize days is that their catalog went away. Back in the DVD era, they had everything. And in the very early streaming era, they still had a huge number of things to watch.
But their current catalog is badly impoverished, and they're just going to recommend the same 30 Netflix originals they always recommend to me, plus a few films or series that are rotating through on a temporary license. If I actually try to search for something specific I'd really like to rewatch, it's almost never there. They haven't quite regressed to the level of a small-town, early 90s Blockbuster, but it sure feels that way sometimes.
So honestly, how much good can the Netflix recommendation algorithm do these days, given the much smaller catalog of movies and films it apparently has to work with?
(The one that I don't get is the Kindle recommendation algorithm. If I read one really good book with a certain theme, the Kindle immediately replaces my recommendations with 40 bad knockoffs, 25% of them clearly AI written. There's apparently no signal for "actually good.")
Have you considered that a majority of the userbase regularly rewatches the same shows again and again as "comfort shows", e.g. tenth rewatch of Friends?
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.
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?)
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
When the Pandora music service launched, I remember reading about how it worked (predicting what you might like next) and it explained how they don't need to know anything about the songs at all (such as genre, tempo, instrumentation, place of origin, year of release, language, etc.) to predict what else you'll like. They simply identify other users with a similar history to you, and deliver to you some of the stuff that those other users had listened to that you hadn't listened to yet. If you abandon a song (suggesting that you don't like it much) then you become a bit less associated with the users who play that song a lot.
I don’t think that works well for movies and tv shows because there is way less datapoints than music (they are way longer, meaning we don’t consume that many films/episodes). A song is also generally more focused, you generally have one tempo, level of energy, and limited instruments. A move on the other hand is a mix of so many art forms. If you only rely on what your cohort voted for you’re missing so much information to make an actual good recommendation.
Netflix system seems to work for their goal, which is to increase revenue, but it’s not a great system for the user satisfaction
You don't think they have a genuine interest in providing good recommendations? A good algorithm will hook viewers more than a bad one. For the algorithm to work, they need to know what you like, and perhaps why you like it.
They can get more accurate results for "what keeps this person watching for longer with more ads" from your exact watching patterns than by asking you. HBO even had a very successful show along these lines.
How many survey participants would truthfully respond with "I liked when they showed nudity"? A video host might instead just see that the nudity scenes are where people gravitate towards with their pauses, plays, fast forwards, and rewinds.
That works for your specific example, not for music, atmosphere, actor performance, editing, sound effects, and all the other things that make a movie something I would be interested to watch.
I’m not saying they have to give us an extremely detailed form, but something better than “yes/no” would be good. I WANT to give them the information, I want them to recommend me good movies to watch!
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
Yeah, I think that’s the cynical and correct answer, they don’t have the explosive growth from last decade and need something to boost their share price
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.
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've not seen any difference between the spotify "AI DJ" and their daily playlists, they literally just pick a song you like and run similar.
The only difference is that every so often the DJ yaps at you and rotates to a different playlist.
It's still absolutely nothing like a DJ, and spotify daily recommendations are still hopeless compared to what Google Music ( rip ) used to do, which actually was able to deliver diverse playlists based on recommendations.
Just chopping between different homogenous blocs is not a good experience.
In my experience, I could choose like, Steve Lacy radio from the song list, and it won't give me anything that sounds like Steve Lacy. It'll give me music that people who like Steve Lacy also like.
But, if I ask the DJ for music like Steve Lacy, I'll get music like Steve Lacy.
I think the LLM basically has a much better semantic understanding of music and how it sounds than the traditional like-based algorithms.
I do agree with you on the base DJ, it just jumps between homogenous blocks, but the impressive thing is asking the DJ for a certain kind of music.
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.
Go ahead and ask it for music that's similar to a song you like. For that use case, I promise it's just way better than anything in the past. Pandora, Spotify Radio, anything.
Their recommendation system was seen as industry standard best in class.
I wouldnt potray them in negative light.
Compared to modern social media platforms they are eons behind ofc social media has more content and more data ofc.
But their BigData and stuff was bleeding edge in pre-2018 in 2026 they really are 5-8 years behind they have not progressed much from what I can tell in both my usage experience, and their technical outputs over the years.
And yes that's the thing about modern development you fall behind really quickly.
Think about it Google was the company to create modern LLMs but people now dog on their LLMs. Apparently even SpaceX is ahead (I don't exactly know but maybe it is).
Being ahead once doesn't mean you are ahead today.
All the world-class data science doesn’t change the fact that the catalog doesn’t actually have many “hidden gem” titles. I find it somewhat hypocritical to argue that naive recommendations skew to the mean, when Netflix will happily pull funding for quirky shows that underperform in front of a general audience.
When was this? 2014?
a nation can emerge as a superpower with advanced infrastructure at one point in time, and still have antiquated infrastructure in the present, no high speed rail and overly complex solutions to make up for it
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.
Nope. This is an internet urban legend. The Netflix prize was only ever for marketing and recruitment. They never intended to deploy whatever winning weights that win because they overfitted on the training and validation data. The dataset they released was not their actual full dataset. It contained zero personal information, just (userid, movieid, rating, date) which is only ever going to get you so far compared to what they do internally.
And it wasn't "a team" that beat the baseline of a ranking algorithm Netflix never deployed, dozens of teams were within fractions of a point from each other. Hence the over fitting strategy.
But their current catalog is badly impoverished, and they're just going to recommend the same 30 Netflix originals they always recommend to me, plus a few films or series that are rotating through on a temporary license. If I actually try to search for something specific I'd really like to rewatch, it's almost never there. They haven't quite regressed to the level of a small-town, early 90s Blockbuster, but it sure feels that way sometimes.
So honestly, how much good can the Netflix recommendation algorithm do these days, given the much smaller catalog of movies and films it apparently has to work with?
(The one that I don't get is the Kindle recommendation algorithm. If I read one really good book with a certain theme, the Kindle immediately replaces my recommendations with 40 bad knockoffs, 25% of them clearly AI written. There's apparently no signal for "actually good.")
Butt this whole era has been about saying, nonono, Ur little people now, just follow the algo.
I want to be able to say NO, and that to be an end of it.
Even Steam store, a paragon of consumer utility, doesn't offer this.
Very frustrating.
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.
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/
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
Netflix system seems to work for their goal, which is to increase revenue, but it’s not a great system for the user satisfaction
How many survey participants would truthfully respond with "I liked when they showed nudity"? A video host might instead just see that the nudity scenes are where people gravitate towards with their pauses, plays, fast forwards, and rewinds.
I’m not saying they have to give us an extremely detailed form, but something better than “yes/no” would be good. I WANT to give them the information, I want them to recommend me good movies to watch!
Are they label as such?
You can use LLMs for things they are a bad fit for. I know someone who uses it like a spreadsheet to add sums of numbers, etc.
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.
What I'm excited for is Bumble's AI matchmaker. LLM categorization may actually be the key to a decent dating app.
The only difference is that every so often the DJ yaps at you and rotates to a different playlist.
It's still absolutely nothing like a DJ, and spotify daily recommendations are still hopeless compared to what Google Music ( rip ) used to do, which actually was able to deliver diverse playlists based on recommendations.
Just chopping between different homogenous blocs is not a good experience.
But, if I ask the DJ for music like Steve Lacy, I'll get music like Steve Lacy.
I think the LLM basically has a much better semantic understanding of music and how it sounds than the traditional like-based algorithms.
I do agree with you on the base DJ, it just jumps between homogenous blocks, but the impressive thing is asking the DJ for a certain kind of music.