this post was submitted on 25 Jan 2024
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And their recommendation engine sucks.
Netflix used to be famously good at suggesting films. Articles were written about it, and there was even a cash reward for anyone who could contribute to its performance. Then it just turned to shit.
And the funny thing is that it would have helped counteract the shrinking library. Sure, there would be fewer films on the platform, so you'd be less likely to find a specific title, but at least you could select a film Netflix recommended based on your past ratings and be fairly confident you'd enjoy it. Now? Absolutely not.
My bachelors thesis was basically about recommender systems like this. Netflix truly is a sunken ship.
How do we read it?
If you are just interested in Netflix recommendation algorithms, you could start here
Thanks.
I am in the process of setting up a jellyfin server and was wondering how I would deal with discovery.
Well this can get quite complicated to implement I suppose. I heard letterboxd works nice for discovery if you are lazy, but I don't know if they have a jellyfin plugin.
I will look into them thanks.
It's not widely available and its only in Norwegian, sadly.
However, I will second @mkengine proposal for Letterboxd, I think it is the superior site to nerd out on. Discovery can be a challenge, depending on your own level of investment into the medium. I'm a big ol movie-nerd, and I'm currently grateful to have access to most streaming services through friends/family/partner so I get to browse them if desired.
Apart from that my twitter algorithm is quite skewed towards movies, and I have a "list" on there (curated users you can browse, kind of like a community on here. That's been great.
Other than that, I listed to podcast, sometimes check out our national newspapers reviews (but most of those reviewers are already in the aforementioned twitter-list) etc.
As for reading on recommender systems and the algorithm for netflix. My work was based around bias and "trust" when it comes to the recommender systems and how much it recommended/pushed "its own agenda" to users despite having differential tastes.
Good keywords I enjoyed was: recommender system bias I also read some good articles on the spotify recommender systems. But those mostly centered around people growing attached to their algorhitms. It was a fun read.