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

Wenwei Liang; Wei Zhang; Xiaoling Wang

doi:10.1007/978-3-030-18590-9_44

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

Wenwei Liang
, Wei Zhang^*
, Xiaoling Wang

^*Corresponding author for this work

School of Computer Science and Technology

East China Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2019 International Workshops
Subtitle of host publication	BDMS, BDQM, and GDMA, Proceedings
Editors	Guoliang Li, Jun Yang, Joao Gama, Juggapong Natwichai, Yongxin Tong
Publisher	Springer Verlag
Pages	353-357
Number of pages	5
ISBN (Print)	9783030185893
DOIs	https://doi.org/10.1007/978-3-030-18590-9_44
State	Published - 2019
Event	24th International Workshops on Database Systems for Advanced Applications, DASFAA 2019: BDMS, BDQM, and GDMA - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019

Publication series

Name	Lecture Notes in Computer Science
Volume	11448 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Workshops on Database Systems for Advanced Applications, DASFAA 2019: BDMS, BDQM, and GDMA
Country/Territory	Thailand
City	Chiang Mai
Period	22/04/19 → 25/04/19

Keywords

Check-in prediction
Deep recurrent modeling
Multi-task learning
Temporal point process

Access to Document

10.1007/978-3-030-18590-9_44

Cite this

Liang, W., Zhang, W., & Wang, X. (2019). Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction. In G. Li, J. Yang, J. Gama, J. Natwichai, & Y. Tong (Eds.), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings (pp. 353-357). (Lecture Notes in Computer Science; Vol. 11448 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_44

Liang, Wenwei ; Zhang, Wei ; Wang, Xiaoling. / Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction. Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. editor / Guoliang Li ; Jun Yang ; Joao Gama ; Juggapong Natwichai ; Yongxin Tong. Springer Verlag, 2019. pp. 353-357 (Lecture Notes in Computer Science).

@inproceedings{c5716de53ce04b0ebc39cb33df4c5e98,

title = "Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction",

abstract = "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.",

keywords = "Check-in prediction, Deep recurrent modeling, Multi-task learning, Temporal point process",

author = "Wenwei Liang and Wei Zhang and Xiaoling Wang",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 24th International Workshops on Database Systems for Advanced Applications, DASFAA 2019: BDMS, BDQM, and GDMA ; Conference date: 22-04-2019 Through 25-04-2019",

year = "2019",

doi = "10.1007/978-3-030-18590-9\_44",

language = "英语",

isbn = "9783030185893",

series = "Lecture Notes in Computer Science",

publisher = "Springer Verlag",

pages = "353--357",

editor = "Guoliang Li and Jun Yang and Joao Gama and Juggapong Natwichai and Yongxin Tong",

booktitle = "Database Systems for Advanced Applications - DASFAA 2019 International Workshops",

address = "德国",

}

Liang, W, Zhang, W & Wang, X 2019, Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction. in G Li, J Yang, J Gama, J Natwichai & Y Tong (eds), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Lecture Notes in Computer Science, vol. 11448 LNCS, Springer Verlag, pp. 353-357, 24th International Workshops on Database Systems for Advanced Applications, DASFAA 2019: BDMS, BDQM, and GDMA, Chiang Mai, Thailand, 22/04/19. https://doi.org/10.1007/978-3-030-18590-9_44

Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction. / Liang, Wenwei; Zhang, Wei ; Wang, Xiaoling.
Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. ed. / Guoliang Li; Jun Yang; Joao Gama; Juggapong Natwichai; Yongxin Tong. Springer Verlag, 2019. p. 353-357 (Lecture Notes in Computer Science; Vol. 11448 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction

AU - Liang, Wenwei

AU - Zhang, Wei

AU - Wang, Xiaoling

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Check-in prediction

KW - Deep recurrent modeling

KW - Multi-task learning

KW - Temporal point process

UR - https://www.scopus.com/pages/publications/85065389901

U2 - 10.1007/978-3-030-18590-9_44

DO - 10.1007/978-3-030-18590-9_44

M3 - 会议稿件

AN - SCOPUS:85065389901

SN - 9783030185893

T3 - Lecture Notes in Computer Science

SP - 353

EP - 357

BT - Database Systems for Advanced Applications - DASFAA 2019 International Workshops

A2 - Li, Guoliang

A2 - Yang, Jun

A2 - Gama, Joao

A2 - Natwichai, Juggapong

A2 - Tong, Yongxin

PB - Springer Verlag

T2 - 24th International Workshops on Database Systems for Advanced Applications, DASFAA 2019: BDMS, BDQM, and GDMA

Y2 - 22 April 2019 through 25 April 2019

ER -

Liang W, Zhang W , Wang X. Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction. In Li G, Yang J, Gama J, Natwichai J, Tong Y, editors, Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Springer Verlag. 2019. p. 353-357. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-18590-9_44

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

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Keywords

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