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Ad Click Prediction: Learning from Cognitive Style

  • Tingting Cha
  • Shaohua Lian
  • Chenghong ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11588)

Abstract

In the past two decades, online advertising increased rapidly. It is now an integral part of the web experience. In this study, we divide webpages into two types: image-based and text-based webpages. We also differentiate user cognitive style as verbalizers and visualizers. Then we investigate how user’s visual preference and the webpage type jointly influence the click-through rates of online flash ads. Our empirical results indicate that visual preference can significantly increase the click probability of flash ads. In addition, flash ads on text-based webpages are more likely to draw the attention of those users who prefer visual materials than on image-based webpages. Our findings contribute to the literature of online advertising by explaining how cognitive style and page context jointed affect ad click probability and providing guidelines for advertisers to target users more precisely.

Keywords

Cognitive style Webpage type Exposure frequency Online flash ad 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant #71531006, #11571081, and #71471044), the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and the Scientific Research Project of Shanghai Science and Technology Committee (grant #17DZ1101002).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of ManagementFudan UniversityShanghaiChina

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