生理信号和影响作为广告参与度的预测者
Gregor Strle1,2, Andrej Košir1, Urban Burnik1
1User-Adapted Communication and Ambient Intelligence Lab, Faculty of Electrical Engineering, University of Ljubljana, SI 1000 Ljubljana, Slovenia.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究表明,结合皮肤温度,价值和疲劳信号,可以有效预测广告的参与度. 与使用所有可用的生理和情感数据相比,这种方法提高了准确性并减少了复杂性.
科学领域:
- 人与计算机的交互
- 情感计算是一种情感计算.
- 生物识别信号处理
背景情况:
- 了解用户与广告的互动对于营销的有效性至关重要.
- 生理和情感信号为用户反应提供了客观的衡量标准.
- 之前的研究已经探索了各种生物信号用于参与检测,结果不一.
研究的目的:
- 研究结合生理信号 (心率,电皮活动,瞳孔膨胀,皮肤温度) 和情绪状态 (价值,疲劳) 的有效性,以对广告参与进行分类.
- 为了比较不同信号组合和机器学习模型在预测用户参与方面的性能.
- 确定最佳的功能集,以实现强大而高效的广告参与模型.
主要方法:
- 采集了53名年轻成年人的广告参与度的实地真相数据,使用用户参与度量表.
- 使用的生理信号:心率,电皮活动,瞳孔膨胀和皮肤温度.
- 使用了三种渐变增强分类器 (LightGBM,HistGradientBoostingClassifier,XGBoost) 与信号融合技术.
主要成果:
- 在皮肤温度,价值和疲劳 (5 个特征) 的融合上训练的分类器表现优于使用所有 30 个信号的分类器.
- 与使用所有信号 (例如,HGBC = 0.64 ± 0.10) 相比,优化的特征集实现了更高的平均AUC ROC得分 (例如,HGBC = 0.70 ± 0.11).
- 结合皮肤温度的信号融合比其他信号组合表现出更高的性能.
结论:
- 特定的生理信号,特别是皮肤温度,对于建模广告参与度具有高度信息性.
- 精选的情感和生理数据的信号融合提高了预测准确性,同时简化了特征集.
- 这种方法提供了一种更有效和高效的方法来量化用户与数字广告的互动.
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