A review of mathematical approaches to recommendation algorithms: from collaborative filtering to deep learning

Gjurgica Anastasov , Mirjana Kocaleva Vitanova , Biljana Zlatanovska , Marija Miteva

Recommendation algorithms, collaborative filtration, matrix factorization, machine learning, deep learning, recommender systems.

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.

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"A review of mathematical approaches to recommendation algorithms: from collaborative filtering to deep learning ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 7, page no.c92-c101, July-2026, Available :https://ijsdr.org/papers/IJSDR2606210.pdf

Volume 11 Issue 7, July-2026

Pages : c92-c101

Paper Reg. ID: IJSDR_310816

Published Paper Id: IJSDR2606210

Downloads: 00051

Research Area: Science and Technology

Country: Stip, Stip, Macedonia, The Former Yugoslav Republic of

Published Paper PDF: https://ijsdr.org/papers/IJSDR2606210

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2606210

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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