부산대학교 도서관

Recommender systems nowadays are commonly used in various platforms to provide information based on user preferences. In POI recommendation, systems can generally learn the users' short-term and long-term preferences, which are based on sessions and global information. Existing systems, however, usually overlook the mid-term information, which may contain important indications of user preferences. In this work, we propose a session-based POI recommender system based on Graph Neural Network (GNN). In contrast to existing work, our model can learn short-term, long-term, and mid-term preferences at the same time. In order to learn the mid-term item representation, we construct a week graph and process it by a GAT-based graph model. We further use a gate fusion to integrate three temporal dimensions to obtain the hybrid item representation. We conduct experiments with a real-world POI visiting dataset, and the evaluation results show that our model outperforms compared state-of-art models. By adding mid-term information, the prediction accuracy can be improved by 5% compared to the best baseline.