AI Recommendation Engine Redesign

Problem Statement

A streaming platform's recommendation engine has high click-through rates but users report feeling stuck in 'filter bubbles.' Engagement is high but satisfaction scores are declining, and content creators with niche content can't break through.

Scope

• •Analyze the current recommendation system's strengths and weaknesses
• •Define a multi-objective optimization framework (relevance, diversity, freshness, creator equity)
• •Design A/B test methodology for recommendation changes
• •Write a PRD for the recommendation system update
• •Create a dashboard spec for monitoring recommendation quality

Evaluation Rubric

• •Understanding of recommendation system tradeoffs
• •Multi-objective framework quality
• •A/B testing methodology rigor
• •PRD completeness
• •Dashboard design and metric selection