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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).

Building Recommendation Systems Using the Algorithms KNN and SVD