OxygenREC: Instruction-Following Generative Framework for E-commerce Recommendation
Analysis
This paper introduces OxygenREC, an industrial recommendation system designed to address limitations in existing Generative Recommendation (GR) systems. It leverages a Fast-Slow Thinking architecture to balance deep reasoning capabilities with real-time performance requirements. The key contributions are a semantic alignment mechanism for instruction-enhanced generation and a multi-scenario scalability solution using controllable instructions and policy optimization. The paper aims to improve recommendation accuracy and efficiency in real-world e-commerce environments.
Key Takeaways
- •Addresses limitations of traditional and generative recommendation systems.
- •Employs a Fast-Slow Thinking architecture for efficient deep reasoning.
- •Introduces a semantic alignment mechanism for instruction-guided generation.
- •Offers a solution for multi-scenario scalability using controllable instructions and policy optimization.
- •Aims to improve recommendation accuracy, efficiency, and resource utilization in e-commerce.
Reference
“OxygenREC leverages Fast-Slow Thinking to deliver deep reasoning with strict latency and multi-scenario requirements of real-world environments.”