I’m not an ML expert, but my understanding is that a model is going to spit out a list of recommendations — each with a score. As you design your model, you nee

I’m not an ML expert, but my understanding is that a model is going to spit out a list of recommendations — each with a score. As you design your model, you need to figure out what it is your optimizing for. I understand your approach to be more of a heuristic - build a tree like structure by traversing through the channels for each channel. If I’m interested in A, how do you determine where to start traversal in this tree? And how do you pick out a set of recommendations and rank them? I have a cursory understanding of ML. In addition to finding the relevant entities, you need to rank them. An important question is, “what are you optimizing for?” For YouTube, presumably they want to optimize for watch time. If they want to suggest ads, that model might want to optimize for maximizing revenue. I’m going on a tangent here, but when we say “YouTube’s recommendations are bad” we should keep in mind YouTube might be optimizing for revenue... which isn’t the same as optimizing for seconds watched or optimizing for clicks.