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Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In this paper, we address the problem of next check-in time and location prediction, and propose a deep sequential multi-task model, named Personalized Recurrent Point Process with Attention (PRPPA), which seamlessly integrates user static representation learning, dynamic recent check-in behavior modeling, and temporal point process into a unified architecture. An attention mechanism is further included in the intensity function of point process to enhance the capability of explicitly capturing the effect of past check-in events. Through the experiments, we verify the proposed model is effective in location and time prediction.

源语言英语
主期刊名Database Systems for Advanced Applications - DASFAA 2019 International Workshops
主期刊副标题BDMS, BDQM, and GDMA, Proceedings
编辑Yongxin Tong, Juggapong Natwichai, Guoliang Li, Jun Yang, Joao Gama
出版商Springer Verlag
353-357
页数5
ISBN(印刷版)9783030185893
DOI
出版状态已出版 - 2019
活动24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, 泰国
期限: 22 4月 201925 4月 2019

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11448 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
国家/地区泰国
Chiang Mai
时期22/04/1925/04/19

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