Predicting how users will engage with ads can be challenging. Traditionally, advertising companies use cookies and embedded monitoring codes that produce a record of how users engage with ad content. But when it comes to predicting user engagement ahead of time, a novel approach is needed.
Zhong Ding of Xinjiang University and Xing Feng Fan of Sichuan International Studies University demonstrated a new, unique approach to predicting user engagement in a paper written for the 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). Ding and Fan outline how to use a deep learning model to predict user click-through rates on ads. Their solution enables marketers to maximize their ad ROI and reduce the amount of time it takes to train predictive deep learning models.
How AI Interprets User Interaction Data to Gauge Engagement
Ding and Fan’s AI interprets user interaction data to assess engagement by using recurrent neural networks (RNNs). RNNs do this by employing:
Sequential data analysis. This involves the RNNs analyzing historical user behavior, such as their clicks and views. This enables researchers to identify trends regarding how users engage with ad content.
Layered processing. Deep learning models process data using multiple layers, gradually mapping the data up to higher levels of abstraction. In this way, RNNs surface insights that make it clear how users are engaging with ad content.
Prediction of click-through rates. Ding and Fan use RNNs that predict click-through rates, which makes it easier for marketers to know how users will engage with their ads.
Using dropout technology. Ding and Fan use dropout technology, which omits nodes at random during the training process. In this way, it prevents the model from memorizing training data (also known as “overfitting”) because it forces many neurons to learn genuinely useful representations.
One reason RNNs make it easier to accurately gauge user engagement is that they use recurrent connections. This makes it possible for RNNs to leverage both past and present data at the same time. By combining historical and current information, RNNs can do a better job of predicting how users will engage with ad content.
What These Findings Mean for Ad Creatives, Placement, and Timing
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