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

I wouldnt potray them in negative light.

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.

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.

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.

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/

Like, just ask me instead of trying to guess incorrectly

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.

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.