Hi everyone,
I’m looking for clarification as to why we use the dot product as a “similarity” metric for two vectors with collaborative filtering. For example, imagine two users, both with a metric of 0.5. The dot product will be 0.25. They have the same “similarity” rank as two users with values of 0.25 and 1, which are quite far apart. So why use the dot product?