I have a problem concerning Data Science and Machine Learning, and maybe somebody could share a hint on how to accomplish or where to begin with. Thanks in advance. The thing is I have an application that crunches a similarity ranking for every item in the system. The ranking is based on a set of customizable weights that apply to every feature of the item. Currently these weights are defined ad hoc.
In order to change the ranking (because I think certain items should be on top and others on bottom, instead of where they are actually) I need to change those weights. But I want to do that using Machine Learning, with no human interaction. I am being thinking about using perturbation theory and monte carlo stochastic methods to find those perfect ideal weights... Do you think this is the right thing to do? Any alternative ideas? Thanks a lot!