Building and exploiting spatial–temporal knowledge graph for next POI recommendation

• Published in: Knowledge-based systems Vol. 258; p. 109951
• Main Authors: Chen, Wei, Wan, Huaiyu, Guo, Shengnan, Huang, Haoyu, Zheng, Shaojie, Li, Jiamu, Lin, Shuohao, Lin, Youfang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 22.12.2022
• Subjects: Knowledge graph; Knowledge reasoning; Next POI recommendation; Spatial–temporal transfer relation; Knowledge reasoning; Spatial–temporal transfer relation; Next POI recommendation; Knowledge graph
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2022.109951

Next Point-of-Interest (POI) recommendation has shown great value for both users and businesses in the field of location-based services. Many spatial–temporal inferring methods have been developed to perform this task, but the data sparsity of POI trajectories greatly hinders the recommendation performance. Knowledge graphs (KGs) have been demonstrated as an effective way to tackle data sparsity in the general recommendation field by leveraging the valuable information of entities and relations. Yet, few studies have explored applying KGs for the next POI recommendation task because of the following challenges: (1) how to represent the dynamic mobility behaviors of users with the static entities and relations in KGs; and (2) how to utilize the different types of entities and relations in KGs to capture long- and short-term preferences of users. In this work, we investigate building a spatial–temporal KG (STKG) from check-in sequences of users to promote the next POI recommendation, without introducing any external attributes of users and POIs. In STKG, we design a novel spatial–temporal transfer relation to intuitively capture users’ transition patterns between neighboring POIs. Then, based on the STKG, we propose an innovative model, named STKGRec, for the next POI recommendation, which explicitly models long- and short-term preferences of users in an end-to-end manner. In particular, STKGRec learns both the spatial–temporal correlation of consecutive and nonconsecutive visits in the current check-in sequence to comprehensively capture the short-term preferences of users. Extensive experiments on four real-world datasets demonstrate the superiority of STKGRec against the state-of-the-art baseline methods. The code of our proposed model is available athttps://github.com/WeiChen3690/STKGRec.