arXiv 2606.280702026-06-29cs.AI

JD Oxygen AI 商品中心 (Oxygen AIIC) V1：面向商品理解、管理和应用的工业级 LLM/VLM 解决方案

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications

Oxygen AIIC（氧气AIIC）

Oxygen AIIC, Chan Long, Chao Liu, Chaofan Chen, Chaohui Dong, Chunyuan Guo, Danping Liu, Debin Liu, Deping Xiang, Fulai Xu, Guangyue Liu, Hao Li, Huichun Hu, Ji… 展开作者

Oxygen AIIC, Chan Long, Chao Liu, Chaofan Chen, Chaohui Dong, Chunyuan Guo, Danping Liu, Debin Liu, Deping Xiang, Fulai Xu, Guangyue Liu, Hao Li, Huichun Hu, Jian Yang, Jianan Wang, Jianbo Zhao, Jiaoyang Li, Jiaxing Wang, Jinglong Li, Jinjin Guo, Jun Fang, Jun Liu, Kai Zhou, Li Wang, Lili Gao, Liying Chen, Luning Yang, Mengdi Zhou, Pengzhang Liu, Qi Lv, Qianyun Wang, Qixia Jiang, Ruyue Li, Shimu Liang, Shuxing Wang, Sijie Zhang, Siqi Li, Tianhao Gao, Wang Ke, Weihu Huang, Wencan Lai, Wenjie Zhang, Xiaohui Zhang, Xiaojing Dong, Ya Liu, Yifeng Zhang, Yixiang Wang, Yongtai Zhang, Yongyi Liao, Zhaoru Chen, Zhen Chen, Zhiyong Ma, Zhiyuan Liu, Zhongwei Liu, Ziyan Xing

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AI总结：

针对电商平台海量SKU的知识生产与服务挑战，提出基于LLM/VLM的工业级平台Oxygen AIIC，通过本体工程、S2D知识识别架构、自进化模型和统一商品通道，实现高精度、高吞吐的知识生产，并在搜索、推荐等场景取得显著收益。

摘要：

JD.com，作为全球最大的电子商务平台之一，服务超过7亿活跃用户和数百万商家，拥有数百亿SKU的商品目录。在此规模下，高质量、结构化的商品知识支撑着更好的消费者体验、更低的管理成本和更高的运营效率——然而，生产和提供这种知识面临着三个工业级挑战：快速涌现的新概念、海量SKU的高质量知识生产以及多样化的下游需求。为应对这些挑战，我们提出了JD Oxygen AI商品中心（Oxygen AIIC），一个基于LLM/VLM的工业级平台，用于商品知识的生产和服务。Oxygen AIIC围绕四个核心支柱构建：(i) 由高效人机协作驱动的本体工程，支持包含数百万条目的本体的动态演化和敏捷扩展；(ii) “语义搜索后判别”（S2D）知识识别架构，结合吞吐量提升策略，实现面向数百亿SKU的可扩展、可扩展、高吞吐的AI商品库生产；(iii) 自进化的商品理解LLM/VLM，以稳定可控的方式改进，实现94.2%精确率和82.8%召回率的知识生产；(iv) 统一商品通道，作为数据和服务枢纽。Oxygen AIIC目前覆盖数万个京东品类，每天在华为昇腾NPU上处理数亿次商品更新，已积累数千亿商品知识资产。部署在搜索、推荐、运营、品类规划等核心业务场景中，Oxygen AIIC已带来可衡量的规模化收益。搜索流量覆盖率达到80.4%，商品信息质量问题下降37%，商品上架时核心属性的自动填充率超过80%。

英文摘要：

JD$.$com, one of the world's largest e-commerce platforms, serves over 700 million active users and millions of merchants, with a catalog of tens of billions of SKUs. At this scale, high-quality, structured item knowledge underpins a better consumer experience, lower management costs, and higher operational efficiency-yet producing and serving it poses three industrial-scale challenges: fast-emerging concepts, high-quality knowledge production for massive SKUs, and diverse downstream requirements. To address these challenges, we present the JD Oxygen AI Item Center (Oxygen AIIC), an industrial-scale platform built on LLMs/VLMs for item-knowledge production and service. Oxygen AIIC is built around four core pillars: (i) ontology engineering driven by efficient human-AI collaboration, which supports the dynamic evolution and agile expansion of an ontology with millions of entries; (ii) a "Semantic Search then Discrimination"(S2D) knowledge identification architecture that, combined with throughput improvement strategies, enables scalable, extensible, and high-throughput AI Item Library production for tens of billions of SKUs; (iii) self-evolving item-understanding LLMs/VLMs that improve in a stable and controllable manner, enabling knowledge production with 94.2% precision and 82.8% recall; and (iv) a unified item tunnel that serves as the data and service hub. Oxygen AIIC now covers tens of thousands of JD categories and processes hundreds of millions of item updates per day on Huawei Ascend NPUs. It has accumulated hundreds of billions of item-knowledge assets. Deployed across core business scenarios-including search, recommendation, operations, category planning-Oxygen AIIC has delivered measurable gains at scale. Search-traffic coverage reaches 80.4%, item-information quality issues drop by 37%, the automated fill rate of core attributes during item listing exceeds 80%.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。