In the context of recommender systems, what is the primary challenge addressed by matrix factorization techniques?

Matrix factorization techniques primarily address the sparsity problem in recommender systems. In such systems, user-item interaction data is typically sparse, and matrix factorization helps in predicting missing values by factoring the observed data matrix into latent factors. This mitigates the sparsity challenge.