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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 12

User matching method for cross social networks based on spatial-temporal transformer-encoder

Zhang Yang
Ma Qiang
School of Information Engineering, Southwest University of Science & Technology, Mianyang 621010, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0146

Abstract

In response to the shortcomings of current cross social networks user matching methods based on check-in spatial-temporal data that do not fully utilize the coupling relationship between spatial and temporal information, resulting in difficulty in feature extraction from spatial-temporal data and a decrease in matching accuracy. This article proposes a cross social network user matching model based on spatial-temporal Transformer-Encoder. This method converts check-in spatial-temporal information into sequential data through grid mapping, generates check-in sequences; It uses sequence embedding layers to map discrete check-in sequences to a continuous high-dimensional space; Then, it uses multi-head attention mechanism and convolutional neural network to extract high-dimensional check-in features, and uses convolutional neural network to optimize multi-head attention module weight transformation and feature fusion; Finally, it uses feedforward neural networks to implement classification and outputting user matching scores. Extensive user matching experiments on two real social network user datasets show improvements in accuracy by 0.40% to 10.53%, and f1 value by 0.43% to 9.5%, compared to existing methods. The experimental validates that the proposed method can effectively extract user check-in coupling features and improve user matching performance.

Foundation Support

国家自然科学基金面上项目(智能网联汽车社交隐私保护与信任管理机制(62071170)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0146
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 12

Publish History

[2024-09-06] Accepted Paper

Cite This Article

张洋, 马强. 基于时空Transformer-Encoder的跨社交网络用户匹配方法 [J]. 计算机应用研究, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0146. (Zhang Yang, Ma Qiang. User matching method for cross social networks based on spatial-temporal transformer-encoder [J]. Application Research of Computers, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0146. )

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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