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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Based on spatial-temporal enhancement of sequence graph and geographical relationships for points-of-interest recommendation

Liu Chao
Zhu Jun
College of computer science & engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2024.08.0298

Abstract

To address the insufficient of mining geographical feature and the absence of incorporating sequential information into spatial preferences in existing Points-of-Interest (POI) recommendation methods, we propose a POI recommendation model based on Spatial-Temporal Enhancement of Sequence Graph and Geographical Relationships (STESGGR) . Firstly, the model constructs a geographic graph using the location information of POIs and employ Graph Convolutional Networks (GCN) combined with attention mechanisms to capture the geographical features of user visits to POIs. Secondly, it extracts spatio-temporal features from user check-in information to construct a sequence graph enhanced with spatio-temporal information and apply Gated Graph Neural Networks (GGNN) combined with attention mechanisms to capture the spatio-temporal preferences of users visiting POIs. Then, it introduces a common learning optimization framework to learn the complementary information between sequential information and geographical features, further mining geographical characteristics. Finally, it fuses the two types of feature information for POI recommendations through a Multilayer Perceptron (MLP) . Experiments on five real-world datasets demonstrate that the STESGGR model improves by 1.2%-2.7% in AUC and 3.2%-12.4% in Logloss metrics. The results validate that STESGGR performs well in location-based POI recommendations, effectively mining sequential and geographical features and enhancing the recommendation performance.

Foundation Support

2021年重庆市社会科学规划一般项目(2021NDYB101)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0298
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-11] Accepted Paper

Cite This Article

刘超, 朱军. 基于序列图时空增强与地理关系的兴趣点推荐 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0298. (Liu Chao, Zhu Jun. Based on spatial-temporal enhancement of sequence graph and geographical relationships for points-of-interest recommendation [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0298. )

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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