this post was submitted on 25 Jan 2024
974 points (95.2% liked)

Piracy: ꜱᴀɪʟ ᴛʜᴇ ʜɪɢʜ ꜱᴇᴀꜱ

55064 readers
131 users here now

⚓ Dedicated to the discussion of digital piracy, including ethical problems and legal advancements.

Rules • Full Version

1. Posts must be related to the discussion of digital piracy

2. Don't request invites, trade, sell, or self-promote

3. Don't request or link to specific pirated titles, including DMs

4. Don't submit low-quality posts, be entitled, or harass others



Loot, Pillage, & Plunder

📜 c/Piracy Wiki (Community Edition):


💰 Please help cover server costs.

Ko-Fi Liberapay
Ko-fi Liberapay

founded 2 years ago
MODERATORS
 
you are viewing a single comment's thread
view the rest of the comments
[–] [email protected] 1 points 11 months ago (2 children)

Thanks.

I am in the process of setting up a jellyfin server and was wondering how I would deal with discovery.

[–] [email protected] 1 points 11 months ago (1 children)

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.

[–] [email protected] 1 points 11 months ago

I will look into them thanks.

[–] [email protected] 1 points 10 months ago

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.