Building Recommendation Systems Using the Algorithms KNN and SVD

Abstract

Recommendation systems are used successfully to provide items (example:
movies, music, books, news, images) tailored to user preferences.
Among the approaches proposed, we use the collaborative filtering approach
of finding the information that satisfies the user by using the
reviews of other users. These ratings are stored in matrices that their
sizes increase exponentially to predict whether an item is interesting
or not. The problem is that these systems overlook that an assessment
may have been influenced by other factors which we call the cold start
factor. Our objective is to apply a hybrid approach of recommendation
systems to improve the quality of the recommendation. The advantage
of this approach is the fact that it does not require a new algorithm
for calculating the predictions. We we are going to apply the two Kclosest
neighbor algorithms and the matrix factorization algorithm of
collaborative filtering which are based on the method of (singular value
decomposition).