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