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

被引：9

作者：

Liang, Wenwei ^{[1
]}

Zhang, Wei ^{[1
]}

Wang, Xiaoling ^{[1
]}

机构：

[1] East China Normal Univ, Shanghai, Peoples R China

来源：

DATABASE SYSTEMS FOR ADVANCED APPLICATIONS | 2019年 / 11448卷

关键词：

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

D O I：

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

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：353 / 357

页数：5

共 7 条

[1] Recurrent Marked Temporal Point Processes: Embedding Event History to Vector
Du, Nan
Dai, Hanjun
Trivedi, Rakshit
Upadhyay, Utkarsh
Gomez-Rodriguez, Manuel
Song, Le
[J]. KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 1555 - 1564
[2] DeepMove: Predicting Human Mobility with Attentional Recurrent Networks
Feng, Jie
Li, Yong
Zhang, Chao
Sun, Funing
Meng, Fanchao
Guo, Ang
Jin, Depeng
[J]. WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), 2018, : 1459 - 1468
[3] Liang YX, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3428
[4] A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation
Manotumruksa, Jarana
Macdonald, Craig
Ounis, Iadh
[J]. ACM/SIGIR PROCEEDINGS 2018, 2018, : 555 - 564
[5] Qiao Z, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P3520
[6] Recurrent Spatio-Temporal Point Process for Check-in Time Prediction
Yang, Guolei
Cai, Ying
Reddy, Chandan K.
[J]. CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2018, : 2203 - 2211
[7] Zhang J., 2015, P 24 ACM INT C INF K, P1221, DOI DOI 10.1145/2806416.2806564

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