Probabilistic Models for Ad Viewability Prediction on the Web

Chong Wang, Achir Kalra, Li Zhou, Cristian Borcea, Yi Chen

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Online display advertising has becomes a billion-dollar industry, and it keeps growing. Advertisers attempt to send marketing messages to attract potential customers via graphic banner ads on publishers' webpages. Advertisers are charged for each view of a page that delivers their display ads. However, recent studies have discovered that more than half of the ads are never shown on users' screens due to insufficient scrolling. Thus, advertisers waste a great amount of money on these ads that do not bring any return on investment. Given this situation, the Interactive Advertising Bureau calls for a shift toward charging by viewable impression, i.e., charge for ads that are viewed by users. With this new pricing model, it is helpful to predict the viewability of an ad. This paper proposes two probabilistic latent class models (PLC) that predict the viewability of any given scroll depth for a user-page pair. Using a real-life dataset from a large publisher, the experiments demonstrate that our models outperform comparison systems.

Original languageEnglish (US)
Article number7931647
Pages (from-to)2012-2025
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume29
Issue number9
DOIs
StatePublished - Sep 1 2017

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Keywords

  • Computational advertising
  • user behavior
  • viewability prediction

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