A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction

摘要

Point of interest (POI) recommendation is a fundamental task in location-based social networks (LBSN). The increasing proliferation of LBSNs brings about considerable amounts of user-generated check-in data. Such data can significantly contribute to understanding user behaviors, based on which personalized recommendations can be efficiently derived. Spatial and temporal effects are crucial factors in the user’s decision-making for choosing a POI to visit. Most existing methods treat them as two independent features and cannot accurately capture users’ interests. We argue that spatial and temporal effects should be analyzed simultaneously in POI recommendations. To this end, we propose a S patioT emporal heterogeneous information Network (HIN)-based PO I RE commendation model (STORE) to model various heterogeneous context features, e.g., the joint spatiotemporal effects, types of POI, and social relations. Specifically, we defined the spatiotemporal effects entity (St) in HIN to model the joint spatiotemporal effects. Instead of modeling the traditional two-way interaction , we further design a four-way neural interaction model . In this way, our model can effectively mine and extract useful information from the meta-path-based context and spatiotemporal effects, thereby improving recommendation performance. We conduct extensive experiments on two real-world datasets, and the results demonstrate that the STORE model outperforms the best baseline by about 12% in NDCG@5 and 11% in Rec@5.