Factorization Based

Factorization-based methods decompose complex data into simpler, interpretable components, aiming to improve model efficiency, interpretability, and personalization. Current research focuses on developing novel factorization algorithms, such as those incorporating neural networks or leveraging properties of matrix square roots, to address computational bottlenecks and enhance performance in various applications like recommendation systems and large language model compression. These advancements are significant because they enable efficient handling of large datasets, improve the explainability of complex models, and facilitate personalized experiences in diverse fields ranging from natural language processing to scientific computing.