  Recommender systems are a kind of data filtering that guides the user to
interesting and valuable resources within an extensive dataset. by providing
suggestions of products that are expected to match their preferences. However,
due to data overloading, recommender systems struggle to handle large volumes
of data reliably and accurately before offering suggestions. The main purpose
of this work is to address the recommender system's data sparsity and accuracy
problems by using the matrix factorization algorithm of collaborative filtering
based on the dimensional reduction method and, more precisely, the Nonnegative
Matrix Factorization (NMF) combined with ontology. We tested the method and
compared the results to other classic methods. The findings showed that the
implemented approach efficiently reduces the sparsity of CF suggestions,
improves their accuracy, and gives more relevant items as recommendations.
