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 proceedingConference contributionpeer-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 languageEnglish
Title of host publication29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
EditorsXiaofeng Meng, Fusheng Wang, Chang-Tien Lu, Yan Huang, Shashi Shekhar, Xing Xie
PublisherAssociation for Computing Machinery
Pages225-228
Number of pages4
ISBN (Electronic)9781450386647
DOIs
Publication statusPublished - 2 Nov 2021
Externally publishedYes
Event29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 - Virtual, Online, China
Duration: 2 Nov 20215 Nov 2021

Publication series

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

Conference

Conference29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
Country/TerritoryChina
CityVirtual, Online
Period2/11/215/11/21

Keywords

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

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