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Webpage Depth Viewability Prediction Using Deep Sequential Neural Networks
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DOI：10.1109/TKDE.2018.2839599.png)
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Abstract
En 中文
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.
Keywords:
Computational advertising
viewability prediction
sequential prediction
recurrent neural networks
user behavior
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