  In this paper, we focus on the problem of modeling dynamic geo-human
interactions in streams for online POI recommendations. Specifically, we
formulate the in-stream geo-human interaction modeling problem into a novel
deep interactive reinforcement learning framework, where an agent is a
recommender and an action is a next POI to visit. We uniquely model the
reinforcement learning environment as a joint and connected composition of
users and geospatial contexts (POIs, POI categories, functional zones). An
event that a user visits a POI in stream updates the states of both users and
geospatial contexts; the agent perceives the updated environment state to make
online recommendations. Specifically, we model a mixed-user event stream by
unifying all users, visits, and geospatial contexts as a dynamic knowledge
graph stream, in order to model human-human, geo-human, geo-geo interactions.
We design an exit mechanism to address the expired information challenge,
devise a meta-path method to address the recommendation candidate generation
challenge, and develop a new deep policy network structure to address the
varying action space challenge, and, finally, propose an effective adversarial
training method for optimization. Finally, we present extensive experiments to
demonstrate the enhanced performance of our method.
