Hi, the author here, we made predictive database to help developers test, prototype and productize predictive functionality - lighting fast.
Just to be clear, the predictive database's value proposition is two fold. First: querying for predictions in instant is much faster than fitting & deploying ML model and using it. Second: it looks like a database and it is used like a database so it is familiar and easy to use.
I am available for any questions, here or via email (antti@aito.ai)
The recommendations are content based. Basically, if you have a preference for certain item or feature, it will get better score.
Compared to collaborative approach: content based scoring works better for learning routine, e.g. the weekly grocery shopping routine. It also works better in situations, where there isn't lot of samples about the recommended content, but there is lots of metadata about options. E.g. the sales situation is such: you likely haven't sold before to this customer company, but you may have lot of information about it
Just to be clear, the predictive database's value proposition is two fold. First: querying for predictions in instant is much faster than fitting & deploying ML model and using it. Second: it looks like a database and it is used like a database so it is familiar and easy to use.
I am available for any questions, here or via email (antti@aito.ai)