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Abstract
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Recommender systems (RSs) have gained significant attention for their ability to model user preferences and predict future trends. Collaborative filtering (CF), particularly through nonnegative matrix factorization (NMF), is a popular method for building these systems. This article presents a comprehensive survey of NMF-based methods in RSs, 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 (INMF) 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 RSs.
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