Content Ranking Platform (Design Contribution)

Digital platforms constantly seek ways to improve how content is ranked and presented to different audiences. The challenge was to explore whether simulated personas could be used to model user behavior, collect meaningful statistics, and guide ranking decisions in a more human‑centric way.

The project focused on designing an AI system that could simulate different types of users, such as elderly audiences, and study how they interact with content. By gathering behavioral data and modeling exposure patterns, the system aimed to optimize ranking strategies and deliver more tailored content experiences.

• – Uses simulated personas to model real-world behavior
• – Collects behavioral statistics on content exposure
• – Ranks and optimizes content experiences based on collected data

• – LLM-based persona simulation
• – Behavioral modeling

The design work demonstrated how persona‑based simulations could make content ranking more adaptive and user‑focused. It provided a framework for testing how different audiences might respond to content, giving teams a practical way to refine ranking strategies and improve overall user experience.