I am trying to come up with a metric to calculate user preference correlation for my final project (a web-shop for shoes) at school. Originally I intended to include user ratings and use Pearson's correlation coefficient but building a rating system might be part of the exam so I have to leave that to the side for now.
I decided to compare purchases directly, taking binary values based on the number of purchases from the first user. This, of course, isn't terribly accurate. So, after looking at the various attributes of the shoes, I am trying to incorporate preferences in brands and style.
For each shoe that is bought by both a 1 is awarded (0 if the shoe is bought only by user1), for brands and styles I was thinking 0.8 and 0.6 respectively.
But then I lose the thread. I am not sure how to combine the variables in a relevant way. I would really appreciate any hints or tips.
(Also, this is a part of the project that I decided to build in myself and is most definitely not required. I am not attempting to cheat by asking here; I just want to learn.)