Using Machine Learning to Measure Digital Audiences

Abstract

In recent years, the field of machine learning has witnessed an unprecedented surge in interest and application across diverse domains. Kantar Media, a leader in audience measurement, aims to apply machine learning to refine and revolutionize audience measurement techniques. This thesis demonstrates the application of recurrent neural networks and attention mechanisms. Despite existing solutions in audience measurement, they exhibit limitations in comprehensively addressing outlier data. The approach demonstrates that leveraging deep learning yields a remarkable test accuracy of 97 percent. However, it is also noted that certain outliers present persistent predictive challenges.