Comment by jkatz05

Blog author. You can choose to use any distance metrics. One reason cosine similarity is popular (and used) is that for many of these higher dimensional datasets, it gives a better representation of "nearness" across all the data basd on the nature of "angular" distance. But depending on how your data is distributed, something like L2 distance (Euclidean) could make more sense.