Webpage Depth Viewability Prediction Using Deep Sequential Neural Networks

Chong Wang, Shuai Zhao, Achir Kalra, Cristian Borcea, Yi Chen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Display advertising is the most important revenue source for publishers in the online publishing industry. The ad pricing standards are shifting to a new model in which ads are paid only if they are viewed. Consequently, an important problem for publishers is to predict the probability that an ad at a given page depth will be shown on a user's screen for a certain dwell time. This paper proposes deep learning models based on Long Short-Term Memory (LSTM) to predict the viewability of any page depth for any given dwell time. The main novelty of our best model consists in the combination of bi-directional LSTM networks, encoder-decoder structure, and residual connections. The experimental results over a dataset collected from a large online publisher demonstrate that the proposed LSTM-based sequential neural networks outperform the comparison methods in terms of prediction performance.

Original languageEnglish (US)
Article number8362690
Pages (from-to)601-614
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume31
Issue number3
DOIs
StatePublished - Mar 1 2019

All Science Journal Classification (ASJC) codes

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

Keywords

  • Computational advertising
  • recurrent neural networks
  • sequential prediction
  • user behavior
  • viewability prediction

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