Research Area:  Machine Learning
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 users 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.
Author(s) Name:  Chong Wang; Shuai Zhao; Achir Kalra; Cristian Borcea and Yi Chen
Journal name:   IEEE Transactions on Knowledge and Data Engineering
Publisher name:  IEEE
Volume Information:  Volume: 31, Issue: 3, March 1 2019, Page(s): 601 - 614
Paper Link:   https://ieeexplore.ieee.org/document/8362690