I am not a data scientist but am trying to implement a recommender system for my company. My application runs on PHP but I will use Python to process the data.
My company is an online school, with 40 online courses as of now. I have a CSV file with around 30k users preferences and it looks like this:
0 means that user is not subscribed (I consider here that they have no interest), while 1 means subscribed (interested).
My idea is to compare one single user array such as [0,1,0,0,0,1,1...] with all this data and return a grade for each course with the probability of interest for this user.
I was thinking of using a Multinomial Logistic Regression, but as far as I know (and I do not know much) it would return me a binary result, right?
What classification model would you recommend me to use? Ideally, my result should be something like:
[0.95, 0.1, 0.54, 0.3, 0.87...]