If we had an open source algorithm for Mastodon/Pixelfed that learned based on the words in the post and image/video we could have a Following + For You feed that showed you all the posts from people you follow and you could choose to see, say, 1 recommended For You post after every 3 posts from your Following feed. With the option to disable For You posts completely or tweak how often you see these.
Discovering new people to follow on mastodon/pixelfed isn’t great (hashtags are rarely used and make posts look ugly) so I still occasionally use twitter because I often discover new indie animators/gamedevs showing off their project making it really nice to browse the For You feed.
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This and the way Mastodon handles likes/favorites are the biggest things holding them back right now, IMO. People don’t want to bother with a social media they actually have to put in the work to curate themselves, hell the algorithm is one of the main reasons why TikTok is so successful. Also, by having favorites (which is… weird and confusing? Just call it “likes”, you don’t need to reinvent the wheel here). Not only do people get confused between favoriting something and bookmarking something, but since it’s only counting the favorites from your instance, it makes it look like posts are getting barely any interaction, giving this “screaming into the void” feeling that will cause a lot of users to lose interest. The number of
likesfavorites needs to be cumulative across instances.You’re 100% right. The people who are already on these platforms are the ones that don’t like any algorithm at all, so they tend to be combative about its implementation. I don’t think there will be mainstream appeal until it is easy and fun to use for the average person
There are effective ways to be discovered on Mastodon: by being boosted by others and by using hashtags.
If a user wants to be discovered they should be using hashtags. If they’re not prioritizing discovery then they shouldn’t. And the “consumer” (for lack of a better word) can follow those hashtags, so they appear automatically on their home timeline.
If a user wants to make their followers to know about a person then they should boost their content. That’s how relationship works on real life: Your friend sees something cool and snows it to you.
I know how reposts work. I know how hashtags work. They’re not great way for discovery especially for discovering smaller accounts. I constantly get recommended tiny accounts posting their gamedev or indie anime work through the algorithm. We can have chronological and algorithm on one feed so its the best of both worlds
This seems like it could have negative side effects. For example if you like posts about negative stories and the algorithm interpreted you being supportive of the post as you appreciating negativity it could work to foster a falsely over negative world view in you sending you into a worse mental state. Or if you dislike a post you might not agree with it could lead to you only seeing posts that reinforce your beliefs to the point of radicalization.
That’s not really how most recommendation algorithms work. A good basic algorithm would be showing you content that the people you follow are following or showing popular posts from a certain category that you tend to like
100%. Simply keep chronological order for the boomers on there and give us a For You algo for actual interesting content. I don’t want to keep getting gaslit that it’s supposedly better and more natural that way–It’s boring as hell.
Stuff like this could be added to misskey, Firefish, or whatever other fediverse platforms there are. No need to implement it on Mastodon & piss off the entrenched community that wants no algorithm.
As for the algorithm, im imagining a few dedicated relay servers to sift through mastodon server data using a dedicated llm (thinking like llama or hugging face? I don’t know much about the technicals) to sift through and rate posts by engagement, word choice (evil, demonic, eugenics, crypto, etc) and rate each post topic collectively based on how virtiolic, how much engagement, how many posts, how similar to other posts (for spam), & the topic category & subcategory based off the respective language.
Users who activate the feature would have likes, saves, & etc stored on the client & a hash sent or something with the potential profile focus of the person.
The server weighing the details will prefer to return less seen & posted content very specifically tailored & occasionally more notable & engaged posts that are more general & less charged.
I think the general idea is interesting, & I would try to build it myself (for those who say stop whining & make it yourself), but im here grinding through kaggle learning & udemy so it’d be a while lol
