Non-Negative Matrix Factorization in Recommender Models: Concept, Survey, and Future Direction
Rachana Mehta, Shakti Mishra, Snehanshu Saha · IEEE Access · 2025
Recommender Systems have gained attraction since last two decades owing to its popularity of providing customers with the information of their choice, items they might purchase, movies they might watch and many preferences. This has led recommenders to penetrate almost all e-commerce and industrial applications. The Netflix Prize competition has shown the rising path to recommenders and Singular Value Decomposition (SVD) based factorization techniques. SVD and its variants are considered as one of the efficient approach for recommendation task, especially aligned for collaborative filtering task. However, these SVD approaches remains less interpretable and explainable, due to the presence of negative components in latent features of entities. In a realistic scenario, it is not possible to have negative components in the features. This leads to exploration of factorization techniques focused on positive components only. The non-negative counterpart of SVD, Non-negative Matrix Factorization (NMF) imposes the non-negativity constraint to features, allowing for more interpretable results. It has wide applicability ranging from Bioinformatics, topic modeling to recommenders. Till date, there have been several survey dedicated to core working of NMF. However, there was very limited literature available as to how different NMF works with recommender systems. There was no streamlined survey on NMF based recommender models that explores NMF in depth with advanced domains, case study and future directions. This article presents the insight on NMF based recommender models with focus on their mathematical modeling and taxonomy based on information they use, penalization term and optimization approach. The article offers the NMF based recommender model based on emerging approaches like Deep Learning, Federated Learning, Transformers, Explainability and Dynamicity. The article also brings up the open issues and challenges of NMF models. Further, the article presented an experimental analysis on NMF models with SVD and Neural Network model. The experimental analysis was covered on three benchmarked datasets of recommenders along with stability analysis. The results showcased the performance improvement brought by NMF in recommenders. Through this article, one can plunge into the dynamics of NMF for recommenders.