Context-aware deep model for joint mobility and time prediction

Mobility prediction, which is to predict where a user will arrive based on the user's historical mobility records, has attracted much attention. We argue that it is more useful to know not only where but also when a user will arrive next in many scenarios such as targeted advertising and taxi s...

Full description

Saved in:
Bibliographic Details
Main Authors: Chen, Yile, Long, Cheng, Cong, Gao, Li, Chenliang
Other Authors: School of Computer Science and Engineering
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148163
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-148163
record_format dspace
spelling sg-ntu-dr.10356-1481632021-04-26T06:28:45Z Context-aware deep model for joint mobility and time prediction Chen, Yile Long, Cheng Cong, Gao Li, Chenliang School of Computer Science and Engineering Proceedings of the 13th International Conference on Web Search and Data Mining Engineering::Computer science and engineering::Information systems::Database management Mobility Time Prediction Mobility prediction, which is to predict where a user will arrive based on the user's historical mobility records, has attracted much attention. We argue that it is more useful to know not only where but also when a user will arrive next in many scenarios such as targeted advertising and taxi service. In this paper, we propose a novel context-aware deep model called DeepJMT for jointly performing mobility prediction (to know where) and time prediction (to know when). The DeepJMT model consists of (1) a hierarchical recurrent neural network (RNN) based sequential dependency encoder, which is more capable of capturing a user's mobility regularities and temporal patterns compared to vanilla RNN based models; (2) a spatial context extractor and a periodicity context extractor to extract location semantics and the user's periodicity, respectively; and (3) a co-attention based social & temporal context extractor which could extract the mobility and temporal evidence from social relationships. Experiments conducted on three real-world datasets show that DeepJMT outperforms the state-of-the-art mobility prediction and time prediction methods. Ministry of Education (MOE) Accepted version Cheng Long’s work is supported by MOE Tier 1 RG20/19 (S). 2021-04-26T06:28:45Z 2021-04-26T06:28:45Z 2020 Conference Paper Chen, Y., Long, C., Cong, G. & Li, C. (2020). Context-aware deep model for joint mobility and time prediction. Proceedings of the 13th International Conference on Web Search and Data Mining, 106-114. https://dx.doi.org/10.1145/3336191.3371837 https://hdl.handle.net/10356/148163 10.1145/3336191.3371837 106 114 en RG20/19 (S) © 2020 Association for Computing Machinery (ACM). All rights reserved. This paper was published in Proceedings of the 13th International Conference on Web Search and Data Mining and is made available with permission of Association for Computing Machinery (ACM). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Information systems::Database management
Mobility
Time Prediction
spellingShingle Engineering::Computer science and engineering::Information systems::Database management
Mobility
Time Prediction
Chen, Yile
Long, Cheng
Cong, Gao
Li, Chenliang
Context-aware deep model for joint mobility and time prediction
description Mobility prediction, which is to predict where a user will arrive based on the user's historical mobility records, has attracted much attention. We argue that it is more useful to know not only where but also when a user will arrive next in many scenarios such as targeted advertising and taxi service. In this paper, we propose a novel context-aware deep model called DeepJMT for jointly performing mobility prediction (to know where) and time prediction (to know when). The DeepJMT model consists of (1) a hierarchical recurrent neural network (RNN) based sequential dependency encoder, which is more capable of capturing a user's mobility regularities and temporal patterns compared to vanilla RNN based models; (2) a spatial context extractor and a periodicity context extractor to extract location semantics and the user's periodicity, respectively; and (3) a co-attention based social & temporal context extractor which could extract the mobility and temporal evidence from social relationships. Experiments conducted on three real-world datasets show that DeepJMT outperforms the state-of-the-art mobility prediction and time prediction methods.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Chen, Yile
Long, Cheng
Cong, Gao
Li, Chenliang
format Conference or Workshop Item
author Chen, Yile
Long, Cheng
Cong, Gao
Li, Chenliang
author_sort Chen, Yile
title Context-aware deep model for joint mobility and time prediction
title_short Context-aware deep model for joint mobility and time prediction
title_full Context-aware deep model for joint mobility and time prediction
title_fullStr Context-aware deep model for joint mobility and time prediction
title_full_unstemmed Context-aware deep model for joint mobility and time prediction
title_sort context-aware deep model for joint mobility and time prediction
publishDate 2021
url https://hdl.handle.net/10356/148163
_version_ 1698713753520963584