Recommender Systems Based on Nonnegative Matrix Factorization: A Survey

Recommender systems have gained significant attention for their ability to model user preferences and predict future trends. Collaborative filtering, particularly through Non-negative Matrix Factorization (NMF), is a popular method for building these systems. This paper presents a comprehensive survey of NMF-based methods in recommender systems, exploring enhancements that leverage key features such as sparsity, implicit feedback, and contextual information. We categorize developments into two main directions: pure NMF variants (including constrained, structured, and generalized NMF) and integrated NMF approaches (combining NMF with traditional and deep learning models). Our survey provides researchers and practitioners with a structured overview of the field’s progress, identifies current challenges, and highlights promising directions for future research in NMF-based recommender systems.