LORE: A Large Generative Model for Search Relevance

Chenji Lu; Zhuo Chen; Hui Zhao; Zhiyuan Zeng; Gang Zhao; Junjie Ren; Ruicong Xu; Haoran Li; Songyan Liu; Pengjie Wang; Jian Xu; Bo Zheng

arXiv · 2512.03025

LORE: A Large Generative Model for Search Relevance

Chenji Lu·Zhuo Chen·Hui Zhao·Zhiyuan Zeng·Gang Zhao·Junjie Ren·Ruicong Xu·Haoran Li·Songyan Liu·Pengjie Wang·Jian Xu·Bo Zheng

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Abstract

Achievement. We introduce LORE, a systematic framework for Large Generative Model-based relevance in e-commerce search. Deployed and iterated over three years, LORE achieves a cumulative +27\% improvement in online GoodRate metrics. This report shares the valuable experience gained throughout its development lifecycle, spanning data, features, training, evaluation, and deployment. Insight. While existing works apply Chain-of-Thought (CoT) to enhance relevance, they often hit a performance ceiling. We argue this stems from treating relevance as a monolithic task, lacking principled deconstruction. Our key insight is that relevance comprises distinct capabilities: knowledge and reasoning, multi-modal matching, and rule adherence. We contend that a qualitative-driven decomposition is essential for breaking through current performance bottlenecks. Contributions. LORE provides a complete blueprint for the LLM relevance lifecycle. Key contributions include: (1) A two-stage training paradigm combining progressive CoT synthesis via SFT with human preference alignment via RL. (2) A comprehensive benchmark, RAIR, designed to evaluate these core capabilities. (3) A query frequency-stratified deployment strategy that efficiently transfers offline LLM capabilities to the online system. LORE serves as both a practical solution and a methodological reference for other vertical domains.

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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, Ruicong Xu, Haoran Li, Songyan Liu, Pengjie Wang, Jian Xu, Bo Zheng. 2025-12-02. LORE: A Large Generative Model for Search Relevance. https://arxiv.org/abs/2512.03025

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