GenRec: Towards LLM-Native Recommendation at Netflix

alt227

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.

wolttam

It feels icky to me that they feed a log of what you’ve watched, when, and on what device, over to an LLM just to get back some recommendations. Even though It’s probably exactly the type of thing I would do on a locally hosted LLM

BosunoB

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.

jmbwell

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.

ofjcihen

I’m curious as to what readers think Large Language Models will bring to the recommendations table that our current machine learning doesn’t.

mvkel

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.

lqstuart

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?

volkk

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

chuckadams

How much advanced machine intelligence does it take to not put stuff on the recommended list that they know I've already watched ?

motoxpro

I wonder if people were this upset when recsys moved from linear regression to XGBoost.