Recommendation System

Recommendation systems use signals such as behavior, similarity, preferences, context, and item characteristics to rank possible choices. They are widely used in ecommerce, media, search, personalization, and internal decision-support tools. It matters because model performance depends on how data, training, validation, and evaluation are designed, not only on which algorithm is selected.