Affective Computational Advertising Based on Perceptual Metrics

TL;DR

This paper introduces ACAD, a perceptually driven computational advertising framework that optimizes ad placement for genuine perception and recall, validated through user studies showing its superiority over existing methods.

Contribution

The paper presents a novel ACAD framework that incorporates perceptual metrics and user study findings to optimize ad placement for emotional impact and recall.

Findings

ACAD achieves higher ad recall compared to competing approaches.

User study confirms ACAD's effectiveness in genuine ad perception.

Framework optimizes ad placement based on perceptual and emotional metrics.

Abstract

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.