Even though It’s probably exactly the type of thing I would do on a locally hosted LLM
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.
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.
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.
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.
alt227•25m ago
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.