🤖 AI Summary
This study addresses the challenge global retailers face in manually maintaining price consistency and constructing coherent price hierarchies across million-scale product catalogs. To this end, the work proposes a scalable, context-aware multi-agent framework that introduces, for the first time, a multi-agent architecture to the construction of retail price classification systems—commonly known as “Lines and Ladders.” The framework orchestrates multiple specialized large language model agents to collaboratively perform product attribute recognition, multimodal value extraction, and hierarchical grouping, thereby mitigating cognitive overload inherent in single-agent approaches. Experimental results on real-world enterprise data demonstrate that a three-agent system achieves an F1 score of 0.83 on Lines tasks, with over 90% precision and 75% recall for grocery and household categories, and an overall catalog assignment accuracy of 80.2%.
📝 Abstract
Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers. However, with catalogs spanning millions of active items, manual governance of price relationships is infeasible. Inconsistent pricing across item variants distorts customer value perception and cannibalizes sales. To address this, we present a scalable, context-aware Multi-Agent Framework designed to automate the construction of "Lines and Ladders" pricing taxonomies. Our framework employs specialized LLM agents to construct these coherent pricing structures by identifying key attributes, extracting multi-modal values, and applying hierarchical grouping logic. Evaluated on real-world enterprise data and deployed in production, our 3-Agent system achieves an F1-score of 0.83 for Lines, outperforming single-agent baselines by mitigating cognitive overload. The system achieves >90% precision and >75% recall in Food & Consumables, and 80.2% assignment accuracy in the unstructured General Merchandise catalog.