English(EN) Beyond Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising

新的模拟器DASH通过跨域用户模拟改进广告系统评估

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-29 13:25

研究人员开发了DASH，这是一种新颖的面向决策的用户模拟器，旨在改进在线广告和推荐系统的评估。与以往仅关注点击等可观察行为并使用单一域历史的模拟器不同，DASH整合了异构的跨域数据，并生成思考痕迹和行为动作。这种方法旨在提供用户偏好的更全面视图，并提高模拟的诊断价值。在腾讯广告的真实世界数据上进行的实验证明了DASH的有效性和效率。 AI

影响提高了在线广告和推荐系统的用户模拟器的保真度和诊断价值。

排序理由该集群包含一篇研究论文，详细介绍了在线广告用户模拟的新方法。[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

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇研究论文，详细介绍了在线广告用户模拟的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 69 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jie Jiang · 2026-07-29 13:25

超越动作模仿：为在线广告学习一个感知决策的用户模拟器

Recent advances in LLM-based user simulation have shown promise for offline evaluation of recommendation and advertising systems. However, existing simulators typically infer user preferences from single-domain interaction histories and are primarily optimized to reproduce observ…

本文如何被排名

报道来源 [1]

超越动作模仿：为在线广告学习一个感知决策的用户模拟器

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