Research Paper

Core Innovation

This paper introduces GenRec, a single decoder-only generative framework that addresses pagination inconsistency, compresses multi-token semantic IDs with a Token Merger, and aligns recommendations with user satisfaction via a novel reinforcement learning method (GRPO-SR). It improves training with a page-wise next-token prediction task for denser supervision and stability.

Why It Matters

Large-scale recommendation systems face challenges in consistency, input encoding costs, and aligning with user preferences, limiting effectiveness. GenRec improves recommendation accuracy and user satisfaction, leading to higher engagement and sales. Its scalable design supports industrial deployment, transforming recommendation workflows for e-commerce platforms.

Potential Customers & Pain Points

• E-commerce platforms – Need scalable accurate recommendation systems
• Online retailers – Struggle with user preference alignment
• Digital marketplaces – Face high computational costs for long user behavior encoding

Business Model

Licensing the GenRec framework as a SaaS API or on-premise solution to e-commerce and digital marketplace companies, with tiered pricing based on traffic volume and feature usage.

Competitive Landscape

• Amazon Personalize
• Google Recommendations AI
• Alibaba PAI
• Microsoft Azure Personalizer

Implementation Challenges

• Integration complexity with existing recommendation pipelines
• Ensuring real-time inference efficiency at scale
• Balancing reinforcement learning stability with production constraints

Validation Strategy

• Conduct extended A/B testing on multiple large e-commerce platforms
• Measure improvements in click-through and transaction rates
• Benchmark computational efficiency and latency against existing systems
• Gather user satisfaction feedback to refine reward models