Linking Mobility Traces of the Same User Across Different Datasets

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Linking Mobility Traces of the Same User Across Different Datasets
Title:
Linking Mobility Traces of the Same User Across Different Datasets
Journal Title:
2023 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)
Keywords:
Publication Date:
14 February 2024
Citation:
Zhang, D., Park, H., & Xiang, S. (2023, December 11). Linking Mobility Traces of the Same User Across Different Datasets. 2023 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). https://doi.org/10.1109/soli60636.2023.10425163
Abstract:
Smart mobile apps have become an integral part of our daily lives, offering intelligent services anytime, anywhere. Various apps tap into mobility traces from different aspects of our daily activities, each managing these traces independently. Linking these isolated traces from different apps offers significant potential for gaining a deeper understanding of user behavior. Various solutions have been proposed to connect mobility traces of the same user across different apps, showing effectiveness in certain scenarios. However, we believe they have not effectively tackled the following four key challenges. First, a solution must accommodate a growing number of users. Second, mobility traces of newly joined users must be successfully linked. Third, auxiliary information, such as place of interests, might not always be accessible. Fourth, activity traces could be extremely sparse. In this paper, we introduce TLink, a deep learning framework designed to link mobility traces of the same user across different datasets, to address the above challenges. TLink employs a temporal convolutional network as its encoder and assesses the probability that a pair of traces, sourced from different datasets, originates from the same user. To train the model effectively, we adapted a multi-step training procedure that strategically modulates the complexity of the training objective. Furthermore, we introduce a new metric learning objective specifically crafted for the trace linking task. Our evaluation demonstrates a notable performance of TLink under challenging scenarios, evidencing a performance improvement of more than tenfold compared to an applicable baseline.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - NRF-NSFC Joint Research Grant Call on Data Science
Grant Reference no. : NRF2016NRF-NSFC001-113
Description:
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
979-8-3503-9600-3
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