Forecasting Ad-Impressions on Online Retail Websites using Non-homogeneous Hawkes Processes

Promotional listing of products or advertisements is a major source of revenue for online retail companies. These advertisements are often sold in the guaranteed delivery market, serving of which critically depends on the ability to predict supply or potential impressions from a target segment of users. In this paper, we study the problem of predicting user visits or potential ad-impressions to online retail websites, based on historical time-stamps. We explore the time-series and temporal point process models. We find that a successful model must encompass three properties of the data: (1) temporally non-homgeneous rates, (2) self excitation and (3) handling special events. We propose a novel non-homogeneous Hawkes process based model for the same, and new algorithm for fitting this model without overfitting the self-excitation part. We validate the proposed model and algorithm using mulitple large scale ad-serving dataset from a top online retail company in India.