Research Paper

Core Innovation

This paper introduces Zenith, a ranking architecture that efficiently models complex feature interactions using Prime Tokens with Token Fusion and Token Boost modules. It achieves superior scaling laws and improved token heterogeneity compared to prior ranking methods, enabling better performance without increasing inference latency.

Why It Matters

Livestreaming platforms face challenges in delivering personalized recommendations efficiently at massive scale. Zenith improves recommendation accuracy and user engagement while maintaining low inference latency, enabling platforms to better monetize and retain users. Its scalable design supports billions of users, transforming how livestream content is surfaced and consumed.

Potential Customers & Pain Points

• Livestreaming platforms – Need scalable accurate recommendation models
• Online video platforms – Require low-latency personalized ranking
• Advertisers – Demand better user engagement metrics
• E-commerce livestreamers – Seek improved content discovery

Business Model

Licensing the ranking model platform to livestreaming and video platforms; offering customization and ongoing optimization services.

Competitive Landscape

• YouTube Recommendation System
• Twitch Recommendation Algorithms
• Facebook Watch Ranking
• Alibaba Livestreaming Recommendations

Implementation Challenges

• Integration complexity with existing large-scale systems
• Maintaining low latency at extreme scale
• Data privacy and compliance challenges

Validation Strategy

• Deploy pilot integrations with mid-sized livestreaming platforms
• Conduct A/B testing to measure CTR
• watch time
• and user engagement improvements
• Iterate model based on real-world performance and latency metrics
• Scale deployment to global platforms with billions of users