Subject: Computer and information sciences
Subject: Matematics
Year: 2026
Type: Article
Type: PeerReviewed
Title: A review of mathematical approaches to recommendation algorithms: from collaborative filtering to deep learning
Author: Anastasov, Gjurgica
Author: Kocaleva, Mirjana
Author: Zlatanovska, Biljana
Author: Miteva, Marija
Abstract: Recommendation algorithms are one of the most important technologies in modern information systems and are widely used in e-commerce, social networks, streaming systems and digital platforms. Their main goal is to provide personalized recommendations by analyzing user preferences and interactions. These systems are based on mathematical concepts from linear algebra, optimization theory, statistics and machine learning. This paper presents an overview of the most important mathematical models and recommendation algorithms, with a special emphasis on collaborative filtration, matrix factorization and modern approaches based on deep learning. Their theoretical foundations, advantages, limitations and evaluation criteria are analyzed. In addition, challenges related to data sparsity, the cold start problem and the computational complexity of the algorithms are considered. The paper provides a systematic overview of the development of recommender systems and identifies future research directions in this area.
Publisher: IJSDR(IJ Publication) Janvi Wave
Relation: https://eprints.ugd.edu.mk/38624/
Identifier: oai:eprints.ugd.edu.mk:38624
Identifier: Anastasov, Gjurgica and Kocaleva, Mirjana and Zlatanovska, Biljana and Miteva, Marija (2026) A review of mathematical approaches to recommendation algorithms: from collaborative filtering to deep learning. IJSDR - International Journal of Scientific Development and Research, 11 (6). pp. 92-101. ISSN 2455-2631
