A recommendation system predicts which titles a given viewer is most likely to watch and enjoy, then surfaces them - in rows, rankings, and artwork - so the catalog feels personal. For large libraries it is the primary way viewers find content; search and browsing handle only a fraction of discovery.
Recommenders blend techniques - collaborative filtering (people like you watched this), content-based filtering (similar to what you watched), and increasingly deep-learning models - fed by viewing history, context, and metadata. Because discovery drives engagement and engagement drives retention, the recommendation system is one of the highest-value pieces of an OTT platform: small improvements in relevance translate directly into watch time and lower churn.
