An Enhanced Ad Event-Prediction Method Based on Feature Engineering
This work addresses the problem of predicting clicks and conversions in digital advertising for advertisers and platforms, but it appears incremental as it builds on existing feature engineering methods.
The paper tackled ad event prediction for digital advertising by proposing a new feature engineering approach, resulting in significant performance improvements over alternative methods, though no concrete numbers were provided.
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.