Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy
TL;DR
A context-aware Multi-Agent Framework was developed to automate the creation of pricing taxonomies for large-scale retail, achieving an F1-score of 0.83. This system significantly improves pricing consistency and customer value perception across millions of items.
✦ Why It Matters
Retailers can implement automated pricing frameworks to enhance pricing consistency and customer satisfaction immediately.
Key Takeaways
- Consider adopting a multi-agent approach for pricing strategies.
- Utilize LLMs to automate complex pricing taxonomy construction.
- Evaluate pricing consistency using metrics like F1-score and precision.
- Implement context-aware systems to enhance customer value perception.
How It Works
The framework employs multiple specialized LLM agents that work collaboratively to analyze product attributes and pricing data. By extracting relevant information and applying hierarchical grouping logic, the agents create structured pricing taxonomies that reflect consistent pricing strategies across various product categories.