Webpage Depth Viewability Prediction Using Deep Sequential Neural Networks

Document Type

Article

Publication Date

3-1-2019

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.

Identifier

85047624393 (Scopus)

Publication Title

IEEE Transactions on Knowledge and Data Engineering

External Full Text Location

https://doi.org/10.1109/TKDE.2018.2839599

e-ISSN

15582191

ISSN

10414347

First Page

601

Last Page

614

Issue

3

Volume

31

Grant

CNS 1409523

Fund Ref

National Science Foundation

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