Matrix Factorization is the correct choice for collaborative filtering tasks in recommendation systems. This technique decomposes the user-item interaction matrix into two matrices that represent users and items in a lower-dimensional space, enabling the model to uncover latent features that reflect user preferences and item attributes. Databricks MLlib includes matrix factorization as part of its collaborative filtering algorithms, making it a suitable option for such tasks.