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Client: StreamFlix
Recommendation Engine
Personalized content recommendation system increasing engagement by 40%
Technologies Used
Collaborative FilteringNeural NetworksPythonAWSReact
Project Overview
StreamFlix needed to improve user engagement and reduce churn on their streaming platform. We built a sophisticated recommendation engine that analyzes viewing history, user preferences, and content metadata to deliver highly personalized recommendations. The system also incorporates collaborative filtering and trending content analysis.
Results & Impact
- •User engagement increased by 40%
- •Average session time increased by 25 minutes
- •Churn rate reduced by 17%
- •Content discovery improved with 65% of users exploring new genres