CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

TL;DR

This paper proposes a novel collaborative filtering recommender system that combines Nonnegative Matrix Factorization with ontology to address data sparsity and improve recommendation accuracy.

Contribution

It introduces an integrated approach using NMF and ontology to enhance the performance of collaborative filtering recommender systems.

Findings

Reduces data sparsity in recommendations

Improves accuracy of suggested items

Provides more relevant recommendations

Abstract

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.