论文标题
基于感知指标的情感计算广告
Affective Computational Advertising Based on Perceptual Metrics
论文作者
论文摘要
我们提出\ textbf {Acad}，\ textbf {a} ffective \ textbf {c} computational \ textbf {ad} vertising框架明确派生自感知指标。与广告方法不同，这些方法要么忽略了（大多数）程序和广告的情感性质，要么基于公理规则，学院的配方纳入了用户研究中的发现，研究了程序内广告位置对广告感知的影响。然后提出了一个线性程序公式，以寻求实现（a）\ emph {furean} ad评估和（b）\ emph {maximal} ad召回。通过有效的用户研究确认了ACAD框架的有效性，在该研究中，发现与竞争方法相对于目标（a）和（b），发现学院引起的广告位置是最佳的。
We present \textbf{ACAD}, an \textbf{a}ffective \textbf{c}omputational \textbf{ad}vertising framework expressly derived from perceptual metrics. Different from advertising methods which either ignore the emotional nature of (most) programs and ads, or are based on axiomatic rules, the ACAD formulation incorporates findings from a user study examining the effect of within-program ad placements on ad perception. A linear program formulation seeking to achieve (a) \emph{genuine} ad assessments and (b) \emph{maximal} ad recall is then proposed. Effectiveness of the ACAD framework is confirmed via a validational user study, where ACAD-induced ad placements are found to be optimal with respect to objectives (a) and (b) against competing approaches.
