I'm very very new to data-science, so go easy on me.
I want to incorporate an SVD based game recommendation model (using the Surprise Python Lib) for a full-stack project I'm working on, but before I jump into it, I want to make sure that I have the right data. I've scraped a bunch of data from Steam using their API, and I have three main columns: user_id, game_id, and time_spent_playing (in minutes). I figure that I can create an 'score' for the game by calculating the standard deviation for each person's playtime, so if they've spent more time playing than the average person, they have a higher score. Is this sufficient for a SVD model? Or is there a better algorithm to use given the data I have?
Bonus question: One of the approaches I've thought about doing my application is to have users rank order their favorite games, and use that as a way to get a better idea as to what their preferences are. For the initial recommendation model, I was thinking that I could arrange the games the users from the training data have based on the score described above, and then use that as the score for the training model. Any thoughts on this would also be useful.
Edit: Calculate z-score, not standard deviation