Leveraging graph neural networks for point-of-interest recommendations

Abstract

Point-of-Interest (POI) recommendation, i.e., suggesting POIs that a user is likely to visit, is a key task to improve user experience in location based social networks (LBSNs). Existing models either focus on geographical influence without considering other factors such as social influence and temporal influence or rely on linear methods to combine different modeling factors, lacking a sophisticated and systematical way to learn representations for users and POIs for recommendation. To remedy these issues, in this work we propose GNN-POI, a generic POI recommendation framework that leverages Graph Neural Networks (GNNs), which demonstrate powerful modeling capacity to learn node representations from node information and topological structure to improve POI recommendation. Specifically, we construct a LBSN graph comprising of two types of nodes, i.e., user node and POI node. For a target user, her preference representation is learned by combining (1) representations of her social connection nodes and (2) representations of the visited POI nodes. For social connection nodes integration, in order to model the complicated and multifaceted social influence, an attention mechanism is applied to learn strengths of heterogeneous social relations; for location nodes integration, we utilize Bi-directional Long Short-Term Memory (Bi-LSTM) to model users’ sequential check-in behavior, taking into account geographical and temporal features. Extensive experiments conducted over three real LBSN datasets show that the proposed GNN based framework significantly outperforms the state-of-the-art POI recommendation models in terms of precision, recall and Normalized Discounted Cumulative Gain (NDCG).