Open AccessRecommendation system using the k-nearest neighbors and singular value decomposition algorithms
Author(s) -
Badr Hssina,
Abdelkader Grota,
Mohammed Erritali
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i6.pp5541-5548
Subject(s) - collaborative filtering , singular value decomposition , computer science , recommender system , matrix decomposition , algorithm , decomposition , data mining , factor (programming language) , quality (philosophy) , matrix (chemical analysis) , k nearest neighbors algorithm , information retrieval , machine learning , ecology , philosophy , eigenvalues and eigenvectors , physics , materials science , epistemology , quantum mechanics , biology , composite material , programming language
Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.