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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Approach for personalized next POI recommendation for spatio-temporal context awareness

Hai Yan1
Wang Jing1
Liu Zhizhong2
1. School of Information Engineering, North China University of Water Resources & Electric Power, Zhengzhou Henan 450045, China
2. School of Computer & Control Engineering, Yantai University, Yantai Shandong 264005, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0539

Abstract

With the rapid development of location-based social networks, the next POI (Point of Interest) recommendation has become a hot research topic in the recommendation field. However, existing research models ignore the spatio-temporal characteristics of POIs and the effect of contextual information on the next POI recommendation. To address this problem, An approach for personalized next POI recommendation for Spatio-Temporal Context Awareness (named STCNPR) . Firstly, STCNPR uses Graph Attention Networks (GAT) to learn user representations containing social relationships. Then, STCNPR applies Prevalence-Enhanced Bipartite Graph Neural Networks (PEBGNN) to learn user representations containing POI interaction preferences and POI representations. Meanwhile, STCNPR applies Spatial Temporal Graph Convolutional Networks (ST-GCN) to learn POI representations containing POI spatio-temporal transfer preferences. We finally fuse the learned user and POI representations to calculate the user's predicted score for each POI and then recommend the next POI to the user based on this score. To validate the method's effectiveness, we test it on three publicly available datasets: Gowalla, Foursquare, and Yelp. The experimental results show that the proposed method demonstrates a significant advantage over several benchmark models in accuracy and recall, with an average improvement of 28.53% and 7.65%, respectively.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0539
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-17] Accepted Paper

Cite This Article

海燕, 王静, 刘志中. 时空上下文感知的下一个POI推荐方法 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0539. (Hai Yan, Wang Jing, Liu Zhizhong. Approach for personalized next POI recommendation for spatio-temporal context awareness [J]. Application Research of Computers, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0539. )

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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