Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
This work addresses the challenge of real-time point-of-interest recommendations for users in location-based services, representing an incremental improvement through novel integration of existing techniques.
The paper tackles the problem of modeling dynamic geo-human interactions for online POI recommendations by proposing a deep interactive reinforcement learning framework that unifies users, visits, and geospatial contexts as a dynamic knowledge graph stream. The method achieves enhanced performance, as demonstrated through extensive experiments.
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.