Cosine similarity vs The Levenshtein distance
I wanted to know what is the difference between them and in what situations they work best?
As per my understanding:
Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. The cosine of 0° is 1, and it is less than 1 for any angle in the interval (0,π] radians.
The Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits
My question is
- When would one use Cosine similarity over The Levenshtein distance?