Efficient Parallel Algorithms for LargeScale Matrix Factorization in Collaborative Filtering Systems

Abstract

Collaborative filtering systems rely heavily on matrix factorization techniques, which often face scalability issues when handling large datasets. This paper presents an efficient parallel algorithm that leverages distributed computing to perform largescale matrix factorization. Experimental results show that our algorithm significantly reduces computation time while maintaining high accuracy. The approach has practical implications for recommendation systems, particularly in ecommerce and social media platforms.