parent is asking about this I think: "This is indeed nice for data visualization, while it’s also very helpful in our pipeline because it removes noise in the derived vectors, by forcing a new mapping based purely on relative similarity. For this reason we will be using the low-dimensional coordinates of each word in our recommender system."

i.e. it's nice for visualisation, and it removes noise. It would be interesting to see discussion from y-hat about where the sweet spot between lack of noise and still keeping relevant information is. I think because the subject matter is pretty simply to cluster, 2D works well enough and keeps everything simple.