BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration
arXiv:2606.04909v1 Announce Type: cross Abstract: E-commerce platforms in emerging markets often operate with underdeveloped product catalogs that contain only category taxonomies but lack structured attribute schemas. This absence of fine-grained product attributes limits search capabilities -- preventing faceted filtering, degrading query understanding, and weakening semantic representations used by search systems. We present BEATS, a human-in-the-loop LLM framework for bootstrapping product attribute taxonomies entirely from scratch. Our approach extends a multi-stage LLM generation pipelin
The proliferation of advanced LLMs enables more sophisticated human-AI collaboration for previously manual and unstructured data tasks.
This development addresses a fundamental challenge in e-commerce by enabling more efficient and comprehensive product information management, which is critical for search, discovery, and personalized experiences.
The ability to bootstrap detailed attribute taxonomies from scratch using LLMs significantly reduces the operational overhead and time required for e-commerce platforms to enrich their product catalogs.
- · E-commerce platforms in emerging markets
- · AI companies specializing in LLM frameworks
- · Consumers seeking better product discovery
- · Manual data annotation services
- · Legacy product information management systems
Improved search relevance and faceted filtering on e-commerce platforms enhance user experience and drive sales.
The enriched product data can be used to train more effective recommender systems and provide deeper product insights for suppliers.
Standardization of product attributes across platforms in regional markets could lead to new avenues for data sharing and market analysis.
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Read at arXiv cs.CL