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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 11

Multi-coding next point of interest recommendation model based on GT model

Wang Yonggui
Zhang Xiaorui
College of Electronics & Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
DOI: 10.19734/j.issn.1001-3695.2024.03.0092

Abstract

Next point of interest (POI) recommendation is a hot topic in the field of recommendation algorithms, aimed at recommending the suitable next locations for users. Recent research has significantly improved performance by simulating user interactions with POIs and the transitions between POIs using graph and sequence methods. However, existing models still have issues that need to be addressed. In response to the limitations of current next POI recommendation models, particularly in how to fully capture both global and local information on the User-POI interaction graph, and in alleviating the oversmoothing characteristics of graph neural networks that lead to information loss on the graph, this paper proposes a multi-coding network based on the Graph Transformer model for recommending the next POI. Firstly, it jointly encodes global, local, and relative information on the User-POI interaction graph from the perspectives of position and structure; then, the graph embeddings produced by this encoding are updated through Graph Transformer network layers, which refresh the information of nodes and edges on the graph; finally, predictions are generated through MLP network layers. The MCGT model is empirically tested on two public datasets, Gowalla and TKY, showing at least a 3.79% improvement in Recall and NDCG metrics on the Gowalla dataset and at least a 2.5% improvement on the TKY dataset, thus proving the reasonableness and effectiveness of the MCGT model design.

Foundation Support

国家自然科学基金面上项目(61772249)
辽宁省教育厅科学研究经费项目(LJKZ0355)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.03.0092
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 11

Publish History

[2024-08-02] Accepted Paper

Cite This Article

王永贵, 张小锐. 基于GT模型的多编码下一个兴趣点推荐模型 [J]. 计算机应用研究, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.03.0092. (Wang Yonggui, Zhang Xiaorui. Multi-coding next point of interest recommendation model based on GT model [J]. Application Research of Computers, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.03.0092. )

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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