Matrix Factorization
Matrix factorization is a family of techniques that decompose a data matrix into lower-dimensional components, aiming to reveal underlying structure and reduce dimensionality. Current research emphasizes developing more efficient and robust algorithms, including those incorporating deep learning, handling missing data, and addressing challenges in distributed and federated settings. These advancements are improving the performance and applicability of matrix factorization across diverse fields, such as recommendation systems, computer vision, and natural language processing, by enabling more accurate and scalable data analysis.
Papers
A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems
Nikzad Chizari, Niloufar Shoeibi, María N. Moreno-García
A variational autoencoder-based nonnegative matrix factorisation model for deep dictionary learning
Hong-Bo Xie, Caoyuan Li, Shuliang Wang, Richard Yi Da Xu, Kerrie Mengersen