There isn't much difference between "Simply 👍/👎" and "scale of 1-5". They will probably give ~same results. I understand first one as {0, 1} and second as {0, ... , 1}. It's just the question of resolution. the 1-5 thing will most likely give you faster convergence, but it can also f you up if some of your data gets mislabeled. Since it's easier to make mistakes with high resolution.

But in a limit, if you take 1 million different people and ask them to asses your model in a binary fashion, or on a scale of 1 to 10, and then average out results, you will get the same thing. It's just from a human perspective, it's easier to asses things as yes-no. (e.g., "did you like this new movie?" vs "how would you rate this movie on a scale from 1 to 10?"). But from computer's perspective, ML wants that label to be as close to its true value as possible.