{"ID":2834148,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03025","arxiv_id":"2512.03025","title":"LORE: A Large Generative Model for Search Relevance","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.","short_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,...","url_abs":"https://arxiv.org/abs/2512.03025","url_pdf":"https://arxiv.org/pdf/2512.03025v3","authors":"[\"Chenji Lu\",\"Zhuo Chen\",\"Hui Zhao\",\"Zhiyuan Zeng\",\"Gang Zhao\",\"Junjie Ren\",\"Ruicong Xu\",\"Haoran Li\",\"Songyan Liu\",\"Pengjie Wang\",\"Jian Xu\",\"Bo Zheng\"]","published":"2025-12-02T18:50:42Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.CL\",\"cs.LG\"]","methods":"[\"Large Language Model\"]","has_code":false}