Predicting Advertisement Clicks Using Deep Networks: Interpreting Deep Learning Models

Deep learning has achieved state-of-the-art results in a variety of tasks such as classifying images and driverless cars. In this paper, I used deep learning to understand consumer product interests. One of the main goals for advertisement agencies is to develop mathematical models to predict whether consumers will click on their advertisement. Achieving the highest click prediction rate means that these agencies can pay to place their online advertisements effectively to target people most interested in their product. Most existing approaches are based on logistic regression or regression tree models (Trofi mov, Kornetova, & Topinskiy, 2012). The model based on deep learning will be discussed to predict the click rate. The data was from the iPinYou competition, where competitors are tasked to build a model that would achieve a high click through rate (CTR). iPinYou provides advertisement data from nine companies. For each instance in the data, various attributes of the person that the electronic advertisement was sent to were provided as well as if the person clicks on the advertisement. I started with exploratory data analysis by splitting data into different seasons, aggregating different advertisers, and cleaning and generating new attributes. I tested my predictive power using a convolutional neural net and a multiple layer perception model. It was shown that the deep learning models have a competitive predictive power and, at the same time, more interpretable for further analysis