SHE is a new RL framework using stepwise hybrid examination rewards to improve reasoning quality and accuracy in large-scale e-commerce query-product relevance prediction.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
K-CARE uses behavior-derived anchoring and expert prototype analogies to ground LLMs and improve relevance on knowledge-intensive e-commerce cases.
citing papers explorer
-
SHE: Stepwise Hybrid Examination Reinforcement Learning Framework for E-commerce Search Relevance
SHE is a new RL framework using stepwise hybrid examination rewards to improve reasoning quality and accuracy in large-scale e-commerce query-product relevance prediction.
-
K-CARE: Knowledge-driven Symmetrical Contextual Anchoring and Analogical Prototype Reasoning for E-commerce Relevance
K-CARE uses behavior-derived anchoring and expert prototype analogies to ground LLMs and improve relevance on knowledge-intensive e-commerce cases.