Definitely not an ML person perspective: One type of recommendation system is user-user recommendation, so it is taking stuff you've (A) looked at, comparing i

Definitely not an ML person perspective: One type of recommendation system is user-user recommendation, so it is taking stuff you've (A) looked at, comparing it to what other's have seen (B), then trying to find what from A that B might like from weighting features of each possible item to find the item that person might like most (and therefore, do the action you want them to: view, buy, etc) So if you view Video 1 (action), Video 3 (action) and Video 4 (comedy). And someone else views Video 4 (comedy), Video 2 (comedy) and Video 3 (action). If we are only using genre as a feature, the system would want to recommend you Video 2 (a comedy you haven't seen) and it would recommend the other user Video 1 (an action video they haven't seen). Your system is introducing new features that could be useful. In my opinion, the reason why recommendation systems suck is either people learning how to game it and therefore have their stuff recommended when their item shouldn't be, the system not having enough data or the system just not being great at figuring out what you like due to bad weights. EDIT: Fully re-reading this post, it seems my post is a bit off-topic. With that said, ML is not a dirty word or has to be some magical thing. It is applying stats to a large dataset. It's not anything fancy nor does it have to use the new shiny thing. ML can be as simple as: go through this list of users and videos, given these weights, what videos would each user likely like most?