Embracing Complexity in Sparse Data: The Power of Weighted Regularized Matrix Factorization (WRMF) in Modern Recommender Systems

Abstract

Context: In the digital media and e-commerce age, recommender systems play a pivotal role in shaping user experience by personalizing content suggestions. Traditional recommendation algorithms often grapple with the inherent sparsity of user-item interaction data.

Problem: One significant challenge these systems face is the prediction accuracy when most user-item interactions…