This is about how Netflix-style “Because you watched…” lists are created. The system watches what you watch, when you stop, what you rewatch, and then predicts what you’re most likely to enjoy next—like a super‑attentive video store clerk who’s seen your entire viewing history.
Viewers are overwhelmed by thousands of titles and leave if they cannot quickly find something engaging. Recommendation engines reduce choice overload, keep users watching longer, and lower churn by surfacing highly relevant movies and shows for each individual user.
Large-scale proprietary user-behavior data (views, skips, completions, ratings), enriched with contextual and content metadata, combined with continuous A/B testing and tightly integrated placement in the streaming UI.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Real-time scoring latency and the cost/complexity of maintaining up-to-date user features at very large scale.
Early Majority
This work focuses on the behavioral and psychological underpinnings of “Because you watched” style recommendations—how users perceive fairness, transparency, and influence on choice—rather than just the engineering side, offering insights for designing more trustworthy and effective recommendation strategies in streaming platforms.
7 use cases in this application