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English(EN) MERIT: Mitigating Exposure Bias in Generative XMC for User-Interest Propensity Modeling

MERIT框架解决了电商AI模型中的暴露偏差问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了MERIT，一个旨在改进大型电商环境中用户兴趣倾向建模的新框架。MERIT解决了自回归语言模型中的“暴露偏差”问题，即早期预测错误可能导致后续输出不准确。通过采用具有排列不变多目标损失的自纠正目标，MERIT训练模型更有效地处理错误的输入。该方法在包含超过25万个兴趣类别的数据集上取得了显著改进，包括全局召回率提高了11.9%，平均Hit@k提高了6.1%，并在生产A/B测试中用户转化率提升了0.26%。 AI

影响 该框架通过改进AI模型对用户兴趣的理解能力，有望提高电商的个性化推荐和转化率。

排序理由 该集群包含一篇详细介绍新框架及其性能指标的研究论文。

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 →

MERIT框架解决了电商AI模型中的暴露偏差问题

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Abhinav Mahajan, Arindam Sarkar, Prakash Mandayam Comar ·

    MERIT：在生成式XMC中缓解暴露偏差以进行用户兴趣倾向建模

    arXiv:2608.28931v1 Announce Type: cross Abstract: Matching users to interest categories at scale is central to personalized shopping, but the task is challenging in large e-commerce platforms, where label spaces continually evolve and user-interest signals are sparse and long-tai…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Prakash Mandayam Comar ·

    MERIT：在生成式XMC中减轻暴露偏差以进行用户兴趣倾向建模

    Matching users to interest categories at scale is central to personalized shopping, but the task is challenging in large e-commerce platforms, where label spaces continually evolve and user-interest signals are sparse and long-tailed. Autoregressive language models are appealing …

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