Unlocking Hidden Patterns: Matrix Factorization Techniques and Their Transformative Role in Data Science
Keywords:
Matrix Factorization, Singular Value Decomposition, Non-negative Matrix Factorization, Alternating Least Squares, Recommender Systems, Dimensionality Reduction, Data Mining, Latent Feature Learning
Abstract
Matrix factorization techniques have become essential in the data science toolkit due to their capability to discover latent structures in high-dimensional datasets. These techniques decompose complex data matrices into simpler, low-rank approximations, allowing for efficient data representation, dimensionality reduction, and pattern discovery. This paper explores three widely used matrix factorization methods— Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), and Alternating Least Squares (ALS)—and their critical applications across domains such as recommendation systems, image processing, and text mining. Through comparative analysis and practical evaluations on benchmark datasets, we highlight the advantages and limitations of each technique. The results demonstrate that matrix factorization not only enhances data interpretability but also enables scalable and accurate predictions in real-world applications, making it a cornerstone of modern data-driven systems.
References
1. Azia, O., & Shaib, I. Data Mining and Pattern Recognition: Unveiling Patterns and Predictive Insights.
2. Adewale, T. (2024). Advanced Tensor Analysis: Unveiling Hidden Patterns with Alternating Least Squares and Emerging Methods.
3. Kalishina, D. (2024). Deep Learning Architectures in Business Analytics: Unlocking Hidden Patterns in Complex Data Streams. International journal of Modern Achievement in Science, Engineering and Technology, 2(1), 133-145.
4. Dhillon, P. S., & Aral, S. (2021). Modeling dynamic user interests: A neural matrix factorization approach. Marketing science, 40(6), 1059-1080.
5. Whig, P., Pansara, R. R., Madavarapu, J. B., Yathiraju, N., & Modhugu, V. R. (2025). Innovative feature engineering methods for graph data science. In Applied Graph Data Science (pp. 119-134). Morgan Kaufmann.
6. Faaique, M. (2024). Overview of big data analytics in modern astronomy. International Journal of Mathematics, Statistics, and Computer Science, 2, 96-113.
7. Roy, P. P., Abdullah, M. S., & Siddique, I. M. (2024). Machine learning empowered geographic information systems: Advancing Spatial analysis and decision making. World Journal of Advanced Research and Reviews, 22(1), 1387-1397.
8. Maindarkar, M. (2025). Application of artificial intelligence in big data management. In Artificial Intelligence in e-Health Framework, Volume 1 (pp. 145- 155). Academic Press.
9. Hu, B., & Wu, Y. (2023). Unlocking Causal Relationships in Commercial Banking Risk Management: An Examination of Explainable AI Integration with Multi-Factor Risk Models. Journal of Financial Risk Management, 12(3), 262-274.
10. Rehan, H. (2023). Artificial intelligence and machine learning: The impact of machine learning on predictive analytics in healthcare. Innovative Computer Sciences Journal, 9(1), 1-20.
11. Slavka, P., & Tatyana, A. (2025). Theoretical Foundations and Practical Applications in Signal Processing and Machine Learning.
12. Nam, Y., Kim, J., Jung, S. H., Woerner, J., Suh, E. H., Lee, D. G., ... & Kim, D. (2024). Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annual Review of Biomedical Data Science, 7.
13. Salem, M., & Shaalan, K. (2025). Unlocking the power of machine learning in E- learning: A comprehensive review of predictive models for student performance and engagement. Education and Information Technologies, 1-24.
14. Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687.
15. Mehmood, K., Jabeen, F., Rashid, M., Alshibani, S. M., Lanteri, A., & Santoro, G. (2024). Unraveling the transformation: the three-wave time-lagged study on big data analytics, green innovation and their impact on economic and environmental performance in manufacturing SMEs. European Journal of Innovation Management.
16. Bhattacherjee, A., & Badhan, A. K. (2024). Convergence of data analytics, big data, and machine learning: applications, challenges, and future direction. In Data analytics and machine learning: navigating the big data landscape (pp. 317-334). Singapore: Springer Nature Singapore.
17. Artene, A. E., Domil, A. E., & Ivascu, L. (2024). Unlocking Business Value: Integrating AI-Driven Decision-Making in Financial Reporting Systems. Electronics (2079-9292), 13(15).
18. Jasinska-Piadlo, A., Bond, R., Biglarbeigi, P., Brisk, R., Campbell, P., Browne, F., & McEneaneny, D. (2023). Data-driven versus a domain-led approach to k-means clustering on an open heart failure dataset. International Journal of Data Science and Analytics, 15(1), 49-66.
2. Adewale, T. (2024). Advanced Tensor Analysis: Unveiling Hidden Patterns with Alternating Least Squares and Emerging Methods.
3. Kalishina, D. (2024). Deep Learning Architectures in Business Analytics: Unlocking Hidden Patterns in Complex Data Streams. International journal of Modern Achievement in Science, Engineering and Technology, 2(1), 133-145.
4. Dhillon, P. S., & Aral, S. (2021). Modeling dynamic user interests: A neural matrix factorization approach. Marketing science, 40(6), 1059-1080.
5. Whig, P., Pansara, R. R., Madavarapu, J. B., Yathiraju, N., & Modhugu, V. R. (2025). Innovative feature engineering methods for graph data science. In Applied Graph Data Science (pp. 119-134). Morgan Kaufmann.
6. Faaique, M. (2024). Overview of big data analytics in modern astronomy. International Journal of Mathematics, Statistics, and Computer Science, 2, 96-113.
7. Roy, P. P., Abdullah, M. S., & Siddique, I. M. (2024). Machine learning empowered geographic information systems: Advancing Spatial analysis and decision making. World Journal of Advanced Research and Reviews, 22(1), 1387-1397.
8. Maindarkar, M. (2025). Application of artificial intelligence in big data management. In Artificial Intelligence in e-Health Framework, Volume 1 (pp. 145- 155). Academic Press.
9. Hu, B., & Wu, Y. (2023). Unlocking Causal Relationships in Commercial Banking Risk Management: An Examination of Explainable AI Integration with Multi-Factor Risk Models. Journal of Financial Risk Management, 12(3), 262-274.
10. Rehan, H. (2023). Artificial intelligence and machine learning: The impact of machine learning on predictive analytics in healthcare. Innovative Computer Sciences Journal, 9(1), 1-20.
11. Slavka, P., & Tatyana, A. (2025). Theoretical Foundations and Practical Applications in Signal Processing and Machine Learning.
12. Nam, Y., Kim, J., Jung, S. H., Woerner, J., Suh, E. H., Lee, D. G., ... & Kim, D. (2024). Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annual Review of Biomedical Data Science, 7.
13. Salem, M., & Shaalan, K. (2025). Unlocking the power of machine learning in E- learning: A comprehensive review of predictive models for student performance and engagement. Education and Information Technologies, 1-24.
14. Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE access, 2, 652-687.
15. Mehmood, K., Jabeen, F., Rashid, M., Alshibani, S. M., Lanteri, A., & Santoro, G. (2024). Unraveling the transformation: the three-wave time-lagged study on big data analytics, green innovation and their impact on economic and environmental performance in manufacturing SMEs. European Journal of Innovation Management.
16. Bhattacherjee, A., & Badhan, A. K. (2024). Convergence of data analytics, big data, and machine learning: applications, challenges, and future direction. In Data analytics and machine learning: navigating the big data landscape (pp. 317-334). Singapore: Springer Nature Singapore.
17. Artene, A. E., Domil, A. E., & Ivascu, L. (2024). Unlocking Business Value: Integrating AI-Driven Decision-Making in Financial Reporting Systems. Electronics (2079-9292), 13(15).
18. Jasinska-Piadlo, A., Bond, R., Biglarbeigi, P., Brisk, R., Campbell, P., Browne, F., & McEneaneny, D. (2023). Data-driven versus a domain-led approach to k-means clustering on an open heart failure dataset. International Journal of Data Science and Analytics, 15(1), 49-66.
Published
2024-09-26
How to Cite
Priyanka Bhargav Patel, Archana Limbachiya, Roshni Ankit Patel, Gaurishankar Gupta, & Kavita Gupta. (2024). Unlocking Hidden Patterns: Matrix Factorization Techniques and Their Transformative Role in Data Science. Revista Electronica De Veterinaria, 25(1S), 1978-1982. https://doi.org/10.69980/redvet.v25i1S.1888
Section
Articles
