By enabling any developer or team to build their own recommendation algorithm, the combination of easier to leverage AI primitives, open protocols, and immutable public data are paving the way for a more personalized and superior user experience (UX) for social applications.
With apps like Twitter, TikTok, and Instagram people are stuck with the given recommendation algorithm without any choices or options other than leave the platform if they don't like it.
For instance a lot of changes have happened with the Twitter recommendation algorithm. People have complained that they see a lot more clickbait, but the only other option is just the "Following" feed or to leave.
In theory, because web3 and blockchain data is public, developers and teams could build their own recommendation algorithms. I said "in theory" because in the past this was not really a thing as this type of system did not really exist before.
We're now starting to see some great alternative recommendation algorithms emerge and become available in the @LensProtocol ecosystem, and apps are already starting to pick them up!
Here are a few teams working on this from various angles (not inclusive of all teams):
Karma3Labs - Is a ranking and reputation infrastructure using the EigenTrust algorithm. It enables developers to curate a ranking and recommendation system for their apps based on on-chain data. karma3labs.com
Whitebox from @kozlovchad - Is an open protocol to consume, develop and collaborate on modular, composable, and transparent algorithms, owned by the developers.
I think of this as something like an NPM for recommendation algorithms
We have a handful more that are also available or being worked on.
Watch this space! It is one of the key differentiators between private, proprietary, and sometimes predatory social networks of the past, and modern social platforms like @LensProtocol