Content Recommendation Engine

The Challenge

A digital media platform was struggling with low user engagement and content discovery. Their existing recommendation system used simple collaborative filtering, which resulted in repetitive suggestions and missed opportunities to surface relevant but less popular content.

Our Solution

We fine-tuned a language model on the platform's content catalog and user interaction data to create a sophisticated recommendation engine. The model understands content semantics, user preferences, and contextual factors to make highly personalized suggestions.

The system balances exploration and exploitation, introducing users to new content while still serving familiar favorites, creating a dynamic discovery experience.

Results

• 3x increase in user engagement with recommended content
• 45% improvement in content discovery metrics
• Reduced content churn rate by 28%
• Increased user session duration by 60%