Deep density networks and uncertainty in recommender systems

Building robust online content recommendation systems requires learning complex interactions between user preferences and content features. Œe €eld has evolved rapidly in recent years from traditional multi-arm bandit and collaborative €ltering techniques, with new methods employing Deep Learning models to capture non-linearities. Despite progress, the dynamic nature of online recommendations still poses great challenges, such as €nding the delicate balance between exploration and exploitation. In this paper we show how uncertainty estimations can be incorporated by employing them in an optimistic exploitation/exploration strategy for more ecient exploration of new recommendations. We provide a novel hybrid deep neural network model, Deep Density Networks (DDN), which integrates content-based deep learning models with a collaborative scheme that is able to robustly model and estimate uncertainty. Finally, we present online and o„ine results a‰er incorporating DNN into a real world content recommendation system that serves billions of recommendations per day, and show the bene€t of using DDN in practice.