Recently picked up recommendation systems and was going through User Based Collaborative Filtering(UB-CF).
Somewhere in the text, it specified that cosine similarity is one of the measures to find similar users and then give a recommendation.
for example a movie recommendation model:
To begin we make a matrix of users and movies filled with the ratings they have provided. now consider
movie1 movie2 movie3 User1 5 5 5 User2 1 1 1
In this case, the cosine similarity will be 1 while both the users have given a very different opinion of the movie and are not similar. How such issues are handled by cosine similarity? Is it not misleading?
PS: I understand that the case is way too hypothetical from a recommendation system point of view where I considered only 3 movies and both the users have watched it while in reality the matrix would be way sparse.