Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 2·5 min read
Autoresearch for Marketplace Catalogs: From Legacy Forms to AI-Native Matching
TL;DR
A new autoresearch loop for service marketplaces transitions from fixed request forms to AI-driven matching using large language models (LLMs). This system, deployed in a major U.S. marketplace, enhances the provider-side preference taxonomy for better service matching across 132 occupations.
✦ Why It Matters
Consider implementing AI-driven matching systems to enhance user experience and service provider engagement in your marketplace.
Key Takeaways
- Explore AI-native matching to improve service provider interactions.
- Utilize iterative refinement cycles for taxonomy generation.
- Implement a scoring framework to evaluate candidate tags.
- Map legacy forms to new taxonomies for better coverage.
How It Works
The autoresearch loop generates a preference taxonomy by treating each occupation as an independent problem. It employs iterative propose-evaluate-keep cycles, where candidate tag sets are scored by a recalibrated LLM framework.
A panel of personas contributes to the scoring process, ensuring diverse perspectives are considered. The system also includes a parity-mapping stage that connects legacy request forms to the new taxonomy, allowing for effective quality assurance.