My bachelors thesis was basically about recommender systems like this. Netflix truly is a sunken ship.
My bachelors thesis was basically about recommender systems like this. Netflix truly is a sunken ship.
There’s a “spinoff” of the skyward series written by a female author! But having really both that trilogy and skyward I’d say chest GPT is off. But it definitely is closer to skyward flight (the spinoff)
Absolutely loving ours like this. But we also still use a bunch of streaming services occationally
Completely fair! There was several moments we agreed to suspend disbelief, and that’s exclusively a negative sign.
That’s what makes it a good sunday show for me, if I’m hungover or tired, maybe I will doze off a bit, but won’t miss much and still get to enjoy some quips and decent action
Me and the missus have binged “Citadel” this past week and its shaping up to be a solid sunday-show! The spinoff coming in 2024 seems fun too!
Around 12 years of reddit here. Completely purged my account and then deleted it. Haven’t been back since.
Noticed that google which is basically a glorified reddit search engine, has become even worse
Is that the "network interface"setting? Bit new and clueless to this
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.