As a follow-up post to my user engagement and content discovery suggestion made yesterday, I have provided a more detailed proposal for the various methodologies that could be used to derive the metrics that make up a user profile.
To create the profile, the data collected may be organized into the following categories (in order of importance) and their data fields:
After working on the five data sets that we get on-chain and off-chain, we have data that we can use to calculate the probability of a person enjoying a post. This is what I call the profile and its data can be used to make queries that ask for a post's score, comparing the post's attributes with the profile's preferred attributes. The following are some of the types of scores that may be queried:
how relevant is the post to the user?
how much value does the post have in comparison with other posts the user likes?
how popular is the post compared to other posts the user likes?
There will need to be a minimum score on all three scores, so that all posts that fall under it won't be recommended to a user; even if the number of recommended posts returns zero and the Discover feed becomes empty. But that's something highly unlikely to occur when the platform has got millions of users all posting and voting.
The profiles may even strengthen each other and another person's similar profile may be used to recommend stuff to a user, at which point the recommendation will be clearly labeled "enjoyed by people you share similar tastes with."
Thanks for reading.
