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Hyper-relational knowledge graph neural network for next POI recommendation

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Abstract

With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These approaches primarily focus on modeling the pair-wise relations in LBSN to enrich the semantics and thereby relieve the data sparsity issue. However, existing approaches seldom consider the hyper-relations in LBSN, such as the mobility relation (a 3-ary relation: user-POI-time). This makes the model hard to exploit the semantics accurately. In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity.To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed to maintain and exploit the rich semantics of hyper-relations. Then we proposed a Hypergraph Neural Network to utilize the structural information of HKG in a cohesive way. In addition, a self-attention network is used to leverage sequential information and make personalized recommendations. Furthermore, side information, essential in reducing data sparsity by providing background knowledge of POIs, is not fully utilized in current methods. In light of this, we extended the current dataset with available side information to further lessen the impact of data sparsity. Results of experiments on four real-world LBSN datasets demonstrate the effectiveness of our approach compared to existing state-of-the-art methods. Our implementation is available at https://github.com/aeroplanepaper/HKG.

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Availability of Data and Materials

The dataset generated in this work is based on a publicly available dataset, and will be released after the acceptance of this paper.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Jixiao Zhang, Ruotong Zou & Jingyuan Zhang

  2. University of Amsterdam, Amsterdam, Netherlands

    Yongkang Li

  3. Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan

    Renhe Jiang

  4. School of Artificial Intelligence, Jilin University, Changchun, China

    Zipei Fan & Xuan Song

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  1. Jixiao Zhang
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  2. Yongkang Li
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  4. Jingyuan Zhang
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  5. Renhe Jiang
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  6. Zipei Fan
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  7. Xuan Song
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Correspondence to Renhe Jiang, Zipei Fan or Xuan Song.

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This article belongs to the Topical Collection: Special Issue on Advancing recommendation systems with foundation models

Guest Editors: Kai Zheng, Renhe Jiang, and Ryosuke Shibasaki

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Zhang, J., Li, Y., Zou, R. et al. Hyper-relational knowledge graph neural network for next POI recommendation. World Wide Web 27, 46 (2024). https://doi.org/10.1007/s11280-024-01279-y

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