What is ensemble learning in large-scale recommendation systems?

Recommendation systems use ensembles to combine collaborative filtering, deep learning, and ranking models.

Modern recommender systems combine multiple models such as matrix factorization, gradient boosting, and deep neural networks. Each model captures different signals: collaborative filtering captures user-item interactions, deep models capture nonlinear patterns, and ranking models optimize business metrics. Ensembles combine these outputs via stacking or weighted scoring to improve relevance and diversity.