Adversarial Mobility Learning for Human Trajectory Classification

Gao, Qiang; Zhang, Fengli; Yao, Fuming; Li, Ailing; Mei, Lin; Zhang, Fan

doi:10.1109/access.2020.2968935

Cited by 17 publications

(4 citation statements)

References 34 publications

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“…Ref. [24] proposed the AdattTUL model to dynamically capture the complex relationship of user check-in from trajectory data. Ref.…”

Section: Related Workmentioning

confidence: 99%

User Re-Identification via Confusion of the Contrastive Distillation Network and Attention Mechanism

Zhang,

Wang,

Zhu

et al. 2023

Sensors

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With the rise of social networks, more and more users share their location on social networks. This gives us a new perspective on the study of user movement patterns. In this paper, we solve the trajectory re-identification task by identifying human movement patterns and then linking unknown trajectories to the user who generated them. Existing solutions generally focus on the location point and the location point information, or a single trajectory, and few studies pay attention to the information between the trajectory and the trajectory. For this reason, in this paper, we propose a new model based on a contrastive distillation network, which uses a contrastive distillation model and attention mechanisms to capture latent semantic information for trajectory sequences and focuses on common key information between pairs of trajectories. Combined with the trajectory library composed of historical trajectories, it not only reduces the number of candidate trajectories but also improves the accuracy of trajectory re-identification. Our extensive experiments on three real-world location-based social network (LBSN) datasets show that our method outperforms existing methods.

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“…Ref. [24] proposed the AdattTUL model to dynamically capture the complex relationship of user check-in from trajectory data. Ref.…”

Section: Related Workmentioning

confidence: 99%

User Re-Identification via Confusion of the Contrastive Distillation Network and Attention Mechanism

Zhang,

Wang,

Zhu

et al. 2023

Sensors

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“…Recently, Gao et al [40] studied the trajectory user linking problem to identify user identities from mobility pa erns. ey combined autoencoder with GANs for jointly human mobility learning, which provides regularized latent space for mobility classi cation.…”

Section: Gans For Trajectory Predictionmentioning

confidence: 99%

Generative Adversarial Networks for Spatio-temporal Data: A Survey

Gao,

Xue,

Shao

et al. 2020

Preprint

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Generative Adversarial Networks (GANs) have shown remarkable success in the computer vision area for producing realistic-looking images. Recently, GAN-based techniques are shown to be promising for spatiotemporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision been presented, nobody has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we conduct a comprehensive review of the recent developments of GANs in spatio-temporal data. we summarise the popular GAN architectures in spatio-temporal data and common practices for evaluating the performance of spatio-temporal applications with GANs. In the end, we point out the future directions with the hope of bene ting researchers interested in this area.

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“…The discriminator tries to maximize the probability of correctly distinguishing the true check-in locations from the generated recommended POIs by the recommender. Gao et al [101] also uses GAN networks for identifying individuals by exploiting their trajectories.…”

Section: Ganmentioning

confidence: 99%

“…Basic RNN -2016 ST-RNN [37] 2018 MCI-DNN [73] 2020 Flashback [38], ASPPA [39], LLRec [40], DRLM [72] LSTM Basic LSTM 2018 HST-LSTM [74], TMCA [42], SLSTM [83] 2019 LSPL [75], ASTEN [77], ATST-LSTM [80], MMR [81], SGBA [82] 2020 PLSPL [76], LSTPM [41], iMTL [43], CatDM [44], ARNN [78], STAR [79] Bi-LSTM 2020 GT-HAN [84], t-LocPred [86], CAPRE [87] Modified LSTM 2020 STGN [88] Self-Attention 2020 GeoSAN [48], SANST [49] GRU -2018 DeepMove [45], CARA [46] 2019 MGRU [47] Graph Embedding -2016 GE [90] 2017 STA [50] 2018 JLGE [92] 2019 RELINE [51] 2020 DYSTAL [52], HCT [96], UP2VEC [98], HMRM [97] GAN -2019 Geo-ALM [99], APOIR [100] 2020 AdattTUL [101] Others -2017 Geo-Teaser [23], LCE [114] 2018 CAPE [102], ST-DME [115] 2019 Bi-STDDP [103], TEMN…”

Section: Category Subcategory Year Referencementioning

confidence: 99%

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Islam¹,

Mohammad²,

Das³

et al. 2020

Preprint

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Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.

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Adversarial Mobility Learning for Human Trajectory Classification

Cited by 17 publications

References 34 publications

User Re-Identification via Confusion of the Contrastive Distillation Network and Attention Mechanism

User Re-Identification via Confusion of the Contrastive Distillation Network and Attention Mechanism

Generative Adversarial Networks for Spatio-temporal Data: A Survey

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

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