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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
398-405

Multi-semantic hypergraph learning with fine-grained spatio-temporal information for next PoI recommendation

Li Wanqiu1
Zhang Chaoqun1,2
Tang Weidong1
Zeng Zhilin1
Li Haoran1
1. College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
2. Guangxi Key Laboratory of Hybrid Computation & IC Design Analysis, Nanning 530006, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0288

Abstract

The current approaches for next point of interest(PoI) recommendation face three main challenges: employing overly simplistic methods to model user interests, neglecting the complex spatio-temporal interactions between users and PoI and failing to fully exploit the intricate higher-order interaction information among users. To address these issues, this paper proposed a novel hypergraph learning model called FSTMH. FSTMH combined temporal, spatial and semantic information carefully. It aimed to understand user preferences and PoI contexts better. FSTMH had two main parts: a fine-grained embedding module and a multi-level embedding module. The fine-grained embedding module used geographic graphs convolutional networks and directed hypergraph convolutional networks. It improved PoI representations through contrastive learning and fine-grained hypergraph convolutional networks. The multi-level embedding module used multi-layer semantic hypergraphs and multi-layer hypergraph convolutional networks. It learned multi-level semantic PoI embedding representations. Extensive experiments were conducted on three widely used social network public datasets and the results show that the FSTMH model performs well, indicating that the new model can be used as an effective method to improve the next PoI recommendation.

Foundation Support

国家自然科学基金资助项目(62062011)
广西民族大学研究生教育创新计划资助项目(gxun-chxs2024117)
广西民族大学研究生教育创新计划资助项目(gxun-chxs2024115)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0288
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Algorithm Research & Explore
Pages: 398-405
Serial Number: 1001-3695(2025)02-010-0398-08

Publish History

[2025-02-05] Printed Article

Cite This Article

李婉秋, 张超群, 汤卫东, 等. 面向下一个兴趣点推荐的细粒度时空多语义超图学习 [J]. 计算机应用研究, 2025, 42 (2): 398-405. (Li Wanqiu, Zhang Chaoqun, Tang Weidong, et al. Multi-semantic hypergraph learning with fine-grained spatio-temporal information for next PoI recommendation [J]. Application Research of Computers, 2025, 42 (2): 398-405. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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