Region-aware POI Recommendation with Semantic Spatial Graph
Jiakai Tang, Jiahui Jin, Zijia Miao, Binjie Zhang, Qi An, Jinghui Zhang
Abstract
The development of Location-Based Social Networks (LBSNs) offers an opportunity for understanding user preferences and promoting Point-of-Interest (POI) recommendation. The user preferences usually change when the environment changes, which will affect the performance of POI recommendation. Many existing methods extract the environmental features from static regions, but they cannot capture user preferences in real time with the fine-grained changes in user locations. Meanwhile, the similarity of POI categories, which is significant to capture user preferences, is usually ignored. To address these issues, we propose RegDM, a region-aware POI recommendation model that employs a semantic spatial graph to model the relations among POIs. With the semantic spatial graph, RegDM uses a Graph Neural Network (GNN) to extract fine-grained region features and user preferences for personalized recommendation. We evaluate RegDM on two datasets, and the experiment results demonstrate the effectiveness of our model.