That's called collaborative filtering (https://en.wikipedia.org/wiki/Collaborative_filtering) and is perhaps the most battle-hardened and most effective approaches in recommender systems. Even now, novel deep learning approaches implement the concept, but simple naive approaches are still as/more effective. The first paper published on it specifically in the field was in the 90s but the seeds of it go back to the 70s. It would have made a good thesis in the 80s :)

Part of why it's so effective are for the reasons you outline, in that it can find items that you'll probably like, that aren't similar to items you already like, based on people that have similar tastes to you.