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English(EN) Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations

大语言模型框架AIR将电子商务推荐速度提升400倍

研究人员开发了一个名为AIR(Atomic Intent Reasoning,原子意图推理)的新框架,以应对将大语言模型(LLMs)应用于工业跨域推荐系统的挑战。该框架通过将大语言模型推理迁移到离线阶段,解决了跨域语义鸿沟和用户行为数据嘈杂等问题。这种方法将推理速度提高了约400倍,同时保持了语义一致性。在真实电子商务环境中进行的大规模A/B测试表明,关键业务指标得到了显著改善,包括GMV(商品交易总额)增长了3.446%。 AI

影响 该框架有望实现大语言模型在实时电子商务推荐系统中的广泛应用,从而提高转化率和用户体验。

排序理由 该集群包含一篇详细介绍新框架及其实验结果的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

大语言模型框架AIR将电子商务推荐速度提升400倍

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhuohang Jiang, Yuxin Chen, Shijie Wang, Haohao Qu, Zhou Jindong, Wenqi Fan, Li Qing, Dongxu Liang, Jun Wang ·

    原子意图推理:将大语言模型语义应用于工业跨领域推荐

    arXiv:2606.10357v1 Announce Type: cross Abstract: Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent on the e-commerce side, thereby enhancing conversio…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jun Wang ·

    原子意图推理:将大语言模型语义应用于工业跨领域推荐

    Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent on the e-commerce side, thereby enhancing conversion rates and commercial value. However, in real ind…