AgenticRS-Architecture: System Design for Agentic Recommender Systems

TL;DR

AutoModel is an agent-based architecture that automates the full lifecycle of industrial recommender systems through interconnected agents with self-improvement capabilities.

Contribution

It introduces a novel agent-based system design for recommender systems, enabling automated, scalable, and adaptive model development and deployment.

Findings

AutoTrain automates paper-driven model reproduction, reducing manual effort.

AutoModel enables localized yet globally coordinated evolution of recommender systems.

The architecture can be generalized to other AI systems like search and advertising.

Abstract

AutoModel is an agent based architecture for the full lifecycle of industrial recommender systems. Instead of a fixed recall and ranking pipeline, AutoModel organizes recommendation as a set of interacting evolution agents with long term memory and self improvement capability. We instantiate three core agents along the axes of models, features, and resources: AutoTrain for model design and training, AutoFeature for data analysis and feature evolution, and AutoPerf for performance, deployment, and online experimentation. A shared coordination and knowledge layer connects these agents and records decisions, configurations, and outcomes. Through a case study of a module called paper autotrain, we show how AutoTrain automates paper driven model reproduction by closing the loop from method parsing to code generation, large scale training, and offline comparison, reducing manual effort for method transfer. AutoModel enables locally automated yet globally aligned evolution of large scale recommender systems and can be generalized to other AI systems such as search and advertising.