English(EN) Beyond Positive Signals: Unlocking Implicit Negative Behaviors for Enhanced Sequential User Modeling

新方法通过利用负面行为信号提升用户建模能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-13 11:18

研究人员开发了一种用于点击率预测中序列用户建模的新方法，该方法将跳过和滚动跳过等隐式负面行为与积极互动结合起来。这种方法称为目标感知极性融合（TAPF），使用轻量级门控机制来区分行为证据，并在各种模型架构中显示出相对 AUC 持续提高了 9.6%。研究强调，主要贡献在于混合极性数据范式本身，它显著优于仅依赖积极信号的模型。 AI

影响通过纳入先前未充分利用的负面行为信号，提高了用户建模的准确性。

排序理由该集群包含一篇发表在 arXiv 上的研究论文，详细介绍了一种用于序列用户建模的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇发表在 arXiv 上的研究论文，详细介绍了一种用于序列用户建模的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jie Jiang · 2026-06-13 11:18

超越积极信号：解锁隐含的负面行为以增强序列用户建模

User behavior sequence modeling has become a central component in modern click-through rate (CTR) prediction. Over the past years, the community has invested substantial effort into improving how sequences are encoded, from target-aware attention and interest evolution networks t…

本文如何被排名

报道来源 [1]

超越积极信号：解锁隐含的负面行为以增强序列用户建模

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