It works…for some definition of “works”. It’s been applied to all kinds of problems—including graphs (Node2Vec) and many other cases where the input isn’t “words”—to the point that I’d consider it a weak baseline for any embedding task. In my experience it is unreasonably effective for simple problems (make a binary classifier for tweets), but the effectiveness drops quickly as the problem gets more complicated.
In your proposed use case I would bet that you will “see” the kind of similarity you’re looking for based on vector similarity, but I also expect it to largely be an illusion due to confirmation bias. It will be much harder to make that similarity actionable to solve the actual business use case. (Like 30% of the time it’ll work like magic; 60% of the time it’ll be “meh”; 10% of the time it’ll be hilariously wrong.)
In your proposed use case I would bet that you will “see” the kind of similarity you’re looking for based on vector similarity, but I also expect it to largely be an illusion due to confirmation bias. It will be much harder to make that similarity actionable to solve the actual business use case. (Like 30% of the time it’ll work like magic; 60% of the time it’ll be “meh”; 10% of the time it’ll be hilariously wrong.)