  In online internet advertising, machine learning models are widely used to
compute the likelihood of a user engaging with product related advertisements.
However, the performance of traditional machine learning models is often
impacted due to variations in user and advertiser behavior. For example, search
engine traffic for florists usually tends to peak around Valentine's day,
Mother's day, etc. To overcome, this challenge, in this manuscript we propose
three models which are able to incorporate the effects arising due to
variations in product demand. The proposed models are a combination of product
demand features, specialized data sampling methodologies and ensemble
techniques. We demonstrate the performance of our proposed models on datasets
obtained from a real-world setting. Our results show that the proposed models
more accurately predict the outcome of users interactions with product related
advertisements while simultaneously being robust to fluctuations in user and
advertiser behaviors.
