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Updated: Jun 12, 2025

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Measuring Delay Discounting in Humans Using an Adjusting Amount Task
Published on: January 9, 2016
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在存在延迟隐性反的情况下,对转换率预测模型进行调整
Taojun Hu1, Xiao-Hua Zhou1,2
1Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Entropy (Basel, Switzerland)
|September 27, 2024
概括
本研究引入了一种新的方法,通过解决选择偏差和延迟反,改善推系统 (RS) 中的转化率 (CVR) 预测. 该方法通过隐式反数据提高了CVR预测的准确性.
科学领域:
- 机器学习 机器学习
- 推系统是一个推系统.
- 在线广告在线广告
背景情况:
- 推系统 (RS) 对在线广告至关重要,其中转化率 (CVR) 预测有助于评估广告影响和用户配置文件.
- 隐式反数据比显式反更为普遍,但在RS中直接使用它可能会导致由于固有的选择偏差而导致低于最佳的性能.
- 现有的方法,比如重权重,解决选择偏差,但往往忽视延迟反,这是有限的观察时间的一个常见问题.
研究的目的:
- 在推系统中开发一种新的CVR预测模型的新方法.
- 在现实场景中有效处理选择偏差和延迟隐性反.
- 提高在线广告CVR预测的准确性和可靠性.
主要方法:
- 一种新的概率方法,将延迟反的参数模型与重权方法相结合,以减轻选择偏差.
- 尽量减少基于概率的损失函数,利用多任务学习.
- 在现实世界数据集 (Coat和Yahoo) 上进行评估,以验证拟议的方法.
主要成果:
- 提出的方法显示,曲线下的面积 (AUC) 显著改善.
- 与基线模型相比,在Coat数据集上实现了5.7%的AUC改进,在Yahoo数据集上实现了3.7%的AUC改进.
- 成功地调试了CVR预测模型,即使存在延迟隐性反.
结论:
- 新型概率方法有效地解决了推系统的CVR预测中的选择偏差和延迟反.
- 提出的方法为提高在线广告平台的性能提供了强大的解决方案.
- 这项研究有助于在推系统中更准确,更可靠的用户行为建模.
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