An ML-powered recommendation engine increasing user engagement by 3x for a digital media platform.
A digital media platform with 2M+ daily users relied on editorial curation for content recommendations, resulting in filter bubbles and stagnant engagement. Their existing collaborative filtering approach cold-started poorly for new users and new content. The system needed to deliver personalized recommendations in under 50ms while handling 50K+ requests per second.
We built a hybrid recommendation engine combining collaborative filtering, content-based similarity, and a transformer-based sequence model for session-level personalization. The ML pipeline runs on Kubernetes with GPU-accelerated inference. A feature store precomputes user and item embeddings, enabling sub-50ms serving. A/B testing infrastructure runs continuous experiments to optimize ranking algorithms. Cold-start problem solved via content embeddings and demographic priors.
User engagement increased 3x within 3 months of deployment. Content discovery rate improved by 156%, exposing users to more diverse content. Average session duration increased by 2.4 minutes. The engine processes 50K+ recommendations per second with 94% relevance accuracy.
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