Inductive Matrix Completion

Inductive matrix completion aims to reconstruct a low-rank matrix from incomplete observations, leveraging side information about its rows and columns. Recent research focuses on developing efficient algorithms, such as those based on Gauss-Newton methods and stochastic variance reduction, to overcome challenges posed by non-convex optimization landscapes and achieve faster convergence. These advancements, supported by theoretical guarantees on generalization and recovery, improve the accuracy and scalability of matrix completion for applications like recommender systems and IoT data analysis. The resulting improvements in accuracy and efficiency are significant for various data-driven applications.