Recommender Systems Based on Nonnegative Matrix Factorization: A Survey
Sajad Ahmadian, Kamal Berahmand, Mehrdad Rostami, Saman Forouzandeh, Parham Moradi, Mahdi Jalili
Abstract
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.