I am working on a Recommendation System as a personal project (I finish it on time, I'll present it as my final year project). I devised mathematical methods that estimated User's ratings of products based on their previous ratings, without using ML. I developed a host of such algorithms for recommendation (I developed a paradigm that allowed me to generate an infinite number of distinct recommendation systems). I intended to assign weights to a select few of them (maybe the ones with the highest accuracy), and take the weighted average of the estimated user ratings.
I realised that Machine Learning could be used to best determine the appropriate weight to assign each of the algorithms on a per user basis, and decided to start learning ML yesterday. It seems what I'm intending to do, is supervised learning.
From what I can tell, it seems existing recommendation systems on the market use unsupervised learning. I am curious to how the two paradigms compare in my case. Would supervised learning work better than unsupervised learning, or is unsupervised learning a better paradigm for recommendation systems?