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.
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.