Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 99-106.doi: 10.11896/jsjkx.240600031

• Database & Big Data & Data Science • Previous Articles     Next Articles

Check-in Trajectory and User Linking Based on Natural Language Augmentation

WANG Tianyi, LIN Youfang, GONG Letian, CHEN Wei, GUO Shengnan, WAN Huaiyu   

  1. School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China
    Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing 100044,China
  • Received:2024-06-04 Revised:2024-08-29 Online:2025-02-15 Published:2025-02-17
  • About author:WANG Tianyi,born in 2000,postgra-duate.His main research interests include spatio-temporal data mining and deep learning.
    WAN Huaiyu,born in 1981,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.17732D).His main research interests include spatial-temporal data mining,information extraction and social networks mining.
  • Supported by:
    National Natural Science Foundation of China(62372031).

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Abstract: With the rapid development of positioning technology and sensors,user movement trajectory data is becoming increa-singly abundant but scattered on different platforms.In order to fully utilize these data and accurately reflect users' real beha-vior,the study of trajectory user linking has become crucial.This task aims to accurately correlate user identities from massive check-in trajectory data.In recent years,researchers have tried to use methods such as recurrent neural networks and attention mechanisms to deeply mine trajectory data.However,current methods face two major challenges when processing user check-in sequences.First,the limited spatiotemporal features in the check-in data are insufficient to comprehensively model check-in point information from both subjective and objective perspectives.Second,the topic of the user check-in sequence will affect understan-ding and modeling check-in sequences.In response to these two challenges,this paper proposes a trajectory user linking model based on natural language augmentation named NLATUL,and designs a set of natural language templates and soft prompt tokens to describe the check-in sequence,and uses the language model to understand the subjective intention in the check-in points,integrating the user's spatiotemporal status,and providing a new perspective and representation that fully models the check-in points from both subjective and objective aspects.On this basis,this paper infer the topic of the check-in sequence through prompt learning,and performs bi-direction encoding on the trajectory represented by the modeled check-in points,so as to achieve an accurate understanding of the user's check-in sequence through the combination of the check-in sequence topic and the check-in sequence encoding,which can link the trajectory with the user more effectively.Verified on two check-in datasets,the experimental results show that proposed method can more accurately link check-in trajectories and their corresponding users.

Key words: Trajectory user link, Check-in sequence learning, Spatiotemporal data mining, Language model, Prompt learning

CLC Number: 

  • TP391
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