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

Netflix was torchbearer of projects that defined thr bigdata and cloud era. They were used as an showcase example for microservice architecture.

Their recommendation system was seen as industry standard best in class.

I wouldnt potray them in negative light.

Netflix is misassigned as a technology company, they are primarily a media company.

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.

> 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.

Why would they not try make money as a business?

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.

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?

Agreed, and of course we need it to respect if we've Seen something elsewhere.

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.

> “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/

This sounds like data they've already been collecting, they're just using it in a different way.

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

1670 is a great show on Netflix. Come to think, it's currently the one great show, which seems to be about as many as they can do at once.

> My wife tends to watch some ai written slop on there as a guilty pleasure

Are they label as such?

It will bring the ability to tell investors that Netflix is using Large Language Models.

I’m curious too, as ML is pretty amazing; LLMs are one application of ML; LLMs have a much narrower good set of uses than broad ML.

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.

Netflix recommendations are bad. So, I expect them to be bad differently.

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.

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.

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.