AI RESEARCH
From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking
arXiv CS.CL
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ArXi:2604.27410v1 Announce Type: cross Entity search, i.e., finding the most similar entities to a query entity, faces unique challenges in e-commerce, where product similarity varies across categories and contexts. Traditional embedding-based approaches often struggle to capture nuanced context-specific attribute relevance. In this paper, we present a two-stage approach combining Large Language Model (LLM)-driven attribute graph construction with graph-aware LLM ranking.