Ads Click-Through Rate prediction using Attention based LSTM Mechanism

Prediction of CTR in online advertisements is crucial to optimize ad campaigns and maximize income for publishers and advertisers in the digital advertising space. Feature classification and high dimensionality are common characteristics of real-world advertising datasets. For appropriate CTR predictions it is needed to model the interaction between the data components of the real-world datasets which are crucial in generating reliable CTR forecasts. Thus, we suggest an attention-based LSTM approach which enhances the capability to manage complex sequences and also identify the long-term patterns in the given data. This paper uses the power of Attention-based LSTM networks to introduce a significant method for CTR prediction. We also gone through attention mechanism's interpretability and how it understands the user behaviour and ad performance. In the field of digital advertising, this research creates new opportunities for improving ad targeting, ad design, and campaign optimization.