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With the advancement of mobile technology, Point of Interest (POI)recommendation systems in Location-based Social Networks (LBSN) have broughtnumerous benefits to both users and companies. Many existing works employKnowledge Graph (KG) to alleviate the data sparsity issue in LBSN. Theseapproaches primarily focus on modeling the pair-wise relations in LBSN toenrich the semantics and thereby relieve the data sparsity issue. However,existing approaches seldom consider the hyper-relations in LBSN, such as themobility relation (a 3-ary relation: user-POI-time). This makes the model hardto exploit the semantics accurately. In addition, prior works overlook the richstructural information inherent in KG, which consists of higher-order relationsand can further alleviate the impact of data sparsity.To this end, we propose aHyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, aHyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructedto maintain and exploit the rich semantics of hyper-relations. Then we proposeda Hypergraph Neural Network to utilize the structural information of HKG in acohesive way. In addition, a self-attention network is used to leveragesequential information and make personalized recommendations. Furthermore, sideinformation, essential in reducing data sparsity by providing backgroundknowledge of POIs, is not fully utilized in current methods. In light of this,we extended the current dataset with available side information to furtherlessen the impact of data sparsity. Results of experiments on four real-worldLBSN datasets demonstrate the effectiveness of our approach compared toexisting state-of-the-art methods.

Hyper-Relational Knowledge Graph Neural Network for Next POI