Adaptive Advertisement Recommender Systems for Digital Signage

With the incorporation of new technologies, digital signages can adopt their content in real time to the audience demographic and temporal features. This research proposes an adaptive advertisement recommender system for digital signage. Our objective is to create a quantitative method for targeted advertising. After analyzing digital signage advertisement viewing data collected over the course of two months, our results show that learning-to-rank approach using Stochastic Gradient-Boosted Trees (SGBT) yields the best adaptive advertisement recommender system. Our system can identify the best sequence of advertisements to attract the most viewing. More importantly, we can use the same method for different business objectives like attracting the longest time of viewing or targeting a certain age groups or genders.