It looks like the cosine similarity of two features is just their dot product scaled by the product of their magnitudes. When does cosine similarity make a better distance metric than the dot product? I.e. do the dot product and cosine similarity have different strengths or weaknesses in different situations?
Cosine similarity versus dot product as distance metrics
classification
Think geometrically. Cosine similarity only cares about angle difference, while dot product cares about angle and magnitude. If you normalize your data to have the same magnitude, the two are indistinguishable. Sometimes it is desirable to ignore the magnitude, hence cosine similarity is nice, but if magnitude plays a role, dot product would be better as a similarity measure. Note that neither of them is a distance metric.