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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 2

Recommendation of disentangled points-of-interest based on spatio-temporal context awareness

Yang Xiaowen1,2,3
Li Jinxiang1,2,3
Kuang Liqun1,2,3
Sun Fusheng1,2,3
Pang Min1,2,3
Li Luyang1,2,3
1. School of Computer Science and Technology, North University of China, Taiyuan 030051, China
2. Shanxi Key Laboratory of Machine Vision & Virtual Reality, Taiyuan 030051, China
3. Shanxi Vision Information Processing and Intelligent Robot Engineering Research Center, Taiyuan 030051, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0237

Abstract

Existing Point-of-Interest (POI) recommendation methods have limitations in spatio-temporal context modeling and interest disentanglement. They struggle to capture multi-level spatio-temporal dependencies, and user interests tend to overlap, which restricts the discovery of diverse interests and long-tail POIs. To address these issues, a spatio-temporal context-aware disentangled POI recommendation model (ST-DPR) was proposed. The model introduced a variational autoencoder module (DIDVAE) based on a Transformer structure, where two independent encoders were used to separately model major and diverse interests, highlighting the structural differences between interest patterns. The model used a hierarchical encoder to capture both local and global spatio-temporal contextual information in check-in sequences, thereby enabling fine-grained modeling of user preferences. The training process integrated cross-entropy loss with the reconstruction loss from the variational autoencoder (VAE) , Kullback–Leibler (KL) divergence, and mutual information loss to improve prediction performance and facilitate effective interest disentanglement. Experiments on three real-world datasets, Foursquare NYC, TKY, and US, demonstrated that ST-DPR outperformed state-of-the-art models in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG) . The results confirm that the proposed model is effective and advantageous for POI prediction tasks.

Foundation Support

山西省科技重大专项计划"揭榜挂帅"项目(202201150401021)
山西省自然科学基金资助项目(202303021212372)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0237
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 2

Publish History

[2025-10-26] Accepted Paper

Cite This Article

杨晓文, 李锦翔, 况立群, 等. 基于时空上下文感知的解纠缠兴趣点推荐 [J]. 计算机应用研究, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0237. (Yang Xiaowen, Li Jinxiang, Kuang Liqun, et al. Recommendation of disentangled points-of-interest based on spatio-temporal context awareness [J]. Application Research of Computers, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0237. )

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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