POI Alias Discovery in Delivery Addresses using User Locations

Tianfu He; Guochun Chen; Chuishi Meng; Huajun He; Zheyi Pan; Yexin Li; Sijie Ruan; Huimin Ren; Ye Yuan; Ruiyuan Li; Junbo Zhang; Jie Bao; Hui He; Yu Zheng

doi:10.1145/3474717.3483950

POI Alias Discovery in Delivery Addresses using User Locations

Tianfu He, Guochun Chen, Chuishi Meng, Huajun He, Zheyi Pan, Yexin Li, Sijie Ruan, Huimin Ren, Ye Yuan, Ruiyuan Li, Junbo Zhang, Jie Bao, Hui He^*, Yu Zheng

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

People often refer to a place of interest (POI) by an alias. In ecommerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.

Original language	English
Title of host publication	29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
Editors	Xiaofeng Meng, Fusheng Wang, Chang-Tien Lu, Yan Huang, Shashi Shekhar, Xing Xie
Publisher	Association for Computing Machinery
Pages	225-228
Number of pages	4
ISBN (Electronic)	9781450386647
DOIs	https://doi.org/10.1145/3474717.3483950
Publication status	Published - 2 Nov 2021
Externally published	Yes
Event	29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 - Virtual, Online, China Duration: 2 Nov 2021 → 5 Nov 2021

Publication series

Name	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference	29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
Country/Territory	China
City	Virtual, Online
Period	2/11/21 → 5/11/21

Keywords

Crowd-sensing
E-Commerce
Geographic Information System
Location Data Mining
Logistics
Urban Computing

Access to Document

10.1145/3474717.3483950

Cite this

He, T., Chen, G., Meng, C., He, H., Pan, Z., Li, Y., Ruan, S., Ren, H., Yuan, Y., Li, R., Zhang, J., Bao, J., He, H., & Zheng, Y. (2021). POI Alias Discovery in Delivery Addresses using User Locations. In X. Meng, F. Wang, C.-T. Lu, Y. Huang, S. Shekhar, & X. Xie (Eds.), 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 (pp. 225-228). (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery. https://doi.org/10.1145/3474717.3483950

He, Tianfu ; Chen, Guochun ; Meng, Chuishi et al. / POI Alias Discovery in Delivery Addresses using User Locations. 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021. editor / Xiaofeng Meng ; Fusheng Wang ; Chang-Tien Lu ; Yan Huang ; Shashi Shekhar ; Xing Xie. Association for Computing Machinery, 2021. pp. 225-228 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

@inproceedings{150bf9ce89f44080ac1d622298ab619d,

title = "POI Alias Discovery in Delivery Addresses using User Locations",

abstract = "People often refer to a place of interest (POI) by an alias. In ecommerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.",

keywords = "Crowd-sensing, E-Commerce, Geographic Information System, Location Data Mining, Logistics, Urban Computing",

author = "Tianfu He and Guochun Chen and Chuishi Meng and Huajun He and Zheyi Pan and Yexin Li and Sijie Ruan and Huimin Ren and Ye Yuan and Ruiyuan Li and Junbo Zhang and Jie Bao and Hui He and Yu Zheng",

note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 ; Conference date: 02-11-2021 Through 05-11-2021",

year = "2021",

month = nov,

day = "2",

doi = "10.1145/3474717.3483950",

language = "English",

series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",

publisher = "Association for Computing Machinery",

pages = "225--228",

editor = "Xiaofeng Meng and Fusheng Wang and Chang-Tien Lu and Yan Huang and Shashi Shekhar and Xing Xie",

booktitle = "29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021",

}

He, T, Chen, G, Meng, C, He, H, Pan, Z, Li, Y, Ruan, S, Ren, H, Yuan, Y, Li, R, Zhang, J, Bao, J, He, H & Zheng, Y 2021, POI Alias Discovery in Delivery Addresses using User Locations. in X Meng, F Wang, C-T Lu, Y Huang, S Shekhar & X Xie (eds), 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Association for Computing Machinery, pp. 225-228, 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021, Virtual, Online, China, 2/11/21. https://doi.org/10.1145/3474717.3483950

POI Alias Discovery in Delivery Addresses using User Locations. / He, Tianfu; Chen, Guochun; Meng, Chuishi et al.
29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021. ed. / Xiaofeng Meng; Fusheng Wang; Chang-Tien Lu; Yan Huang; Shashi Shekhar; Xing Xie. Association for Computing Machinery, 2021. p. 225-228 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

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

TY - GEN

T1 - POI Alias Discovery in Delivery Addresses using User Locations

AU - He, Tianfu

AU - Chen, Guochun

AU - Meng, Chuishi

AU - He, Huajun

AU - Pan, Zheyi

AU - Li, Yexin

AU - Ruan, Sijie

AU - Ren, Huimin

AU - Yuan, Ye

AU - Li, Ruiyuan

AU - Zhang, Junbo

AU - Bao, Jie

AU - He, Hui

AU - Zheng, Yu

PY - 2021/11/2

Y1 - 2021/11/2

N2 - People often refer to a place of interest (POI) by an alias. In ecommerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.

AB - People often refer to a place of interest (POI) by an alias. In ecommerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.

KW - Crowd-sensing

KW - E-Commerce

KW - Geographic Information System

KW - Location Data Mining

KW - Logistics

KW - Urban Computing

UR - http://www.scopus.com/inward/record.url?scp=85119178524&partnerID=8YFLogxK

U2 - 10.1145/3474717.3483950

DO - 10.1145/3474717.3483950

M3 - Conference contribution

AN - SCOPUS:85119178524

T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

SP - 225

EP - 228

BT - 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021

A2 - Meng, Xiaofeng

A2 - Wang, Fusheng

A2 - Lu, Chang-Tien

A2 - Huang, Yan

A2 - Shekhar, Shashi

A2 - Xie, Xing

PB - Association for Computing Machinery

T2 - 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021

Y2 - 2 November 2021 through 5 November 2021

ER -

He T, Chen G, Meng C, He H, Pan Z, Li Y et al. POI Alias Discovery in Delivery Addresses using User Locations. In Meng X, Wang F, Lu CT, Huang Y, Shekhar S, Xie X, editors, 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021. Association for Computing Machinery. 2021. p. 225-228. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). doi: 10.1145/3474717.3483950

POI Alias Discovery in Delivery Addresses using User Locations

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