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.
The more I play it, the more I enjoy it. Sure it has its flaws and definite issues, but they’ve also hit a perfect mid of systems to manage (food, temps, disease etc) where it makes sense and it’s not too overbearing. They’ve also included a hefty amount of welcome QoL changes.
Still, I can’t craft bulk cloth at once etc and some of those things irk me. The procedural (?) generation is a bit repetitive and mob spawns are a complete mess
Been playing Return to Moria with a friend and we’re having a blast! Got to do a few games of The Finals playtest and that was great fun.
Also got to test a few games of CS2 and actually enjoyed that more than expected as well.
Yeah that would be my tip. Once the seasons end in these games, they are transferred over to become non-seasonal characers so nothing is lost.
But that can be a solution for the next one! d3 is such a fun game
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.