In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance. As a result, ad event prediction systems are vital and widely used for sponsored search and display advertising as well as Real-Time Bidding (RTB). In this work, we introduce an enhanced method for ad event prediction (i.e. clicks, conversions) by proposing a new efficient feature engineering approach. A large real-world event-based dataset of a running marketing campaign is used to evaluate the efficiency of the proposed prediction algorithm. The results illustrate the benefits of the proposed ad event prediction approach, which significantly outperforms the alternative ones.
@article{arxiv.1907.01959,
title = {An Enhanced Ad Event-Prediction Method Based on Feature Engineering},
author = {Saeid Soheily Khah and Yiming Wu},
journal= {arXiv preprint arXiv:1907.01959},
year = {2019}
}
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8th International Conference on Soft Computing, Artificial Intelligence and Applications (SAI 2019), June 29-30, 2019, Copenhagen, Denmark