There is a cornucopia of distances to choose from. It's not as simple as "just use the best one." Cosine similarity assumes a certain projection of data (by ign

There is a cornucopia of distances to choose from. It's not as simple as "just use the best one." Cosine similarity assumes a certain projection of data (by ignoring distance of points are from the origin). It preforms well whenever that's a good projection. I think it's best to think of it as one way to reduce dimensionality, and should be used when you think that's the right way to reduce dimensionality.