research-article

AR²Net: An Attentive Neural Approach for Business Location Selection with Satellite Data and Urban Data

Authors:

Jiadi YuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 2

Article No.: 20, Pages 1 - 28

https://doi.org/10.1145/3372406

Published: 09 February 2020 Publication History

Abstract

Business location selection is crucial to the success of businesses. Traditional approaches like manual survey investigate multiple factors, such as foot traffic, neighborhood structure, and available workforce, which are typically hard to measure. In this article, we propose to explore both satellite data (e.g., satellite images and nighttime light data) and urban data for business location selection tasks of various businesses. We extract discriminative features from the two kinds of data and perform empirical analysis to evaluate the correlation between extracted features and the business popularity of locations. A novel neural network approach named R²Net is proposed to learn deep interactions among features and predict the business popularity of locations. The proposed approach is trained with a regression-and-ranking combined loss function to preserve accurate popularity estimation and the ranking order of locations simultaneously. To support the location selection for multiple businesses, we propose an approach named AR²Net with three attention modules, which enable the approach to focus on different latent features according to business types. Comprehensive experiments on a real-world dataset demonstrate that the satellite features are effective and our models outperform the state-of-the-art methods in terms of four metrics.

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Cited By

Gemar GPérez-Benítez V(2024)Review of Business Location Research: a Bibliometric Analysis from 1968 to 2022Revista CENTRA de Ciencias Sociales10.54790/rccs.923:2Online publication date: 3-Jul-2024
https://doi.org/10.54790/rccs.92
Liu YWang WWang HAlidaee B(2023)Quantum Machine Learning on Remote Sensing Data ClassificationJournal of Engineering Research and Sciences10.55708/js02120042:12(23-33)Online publication date: Dec-2023
https://doi.org/10.55708/js0212004
Li TXi YWang HLi YTarkoma SHui P(2023)Learning Representations of Satellite Imagery by Leveraging Point-of-InterestsACM Transactions on Intelligent Systems and Technology10.1145/358934414:4(1-32)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3589344
Show More Cited By

Index Terms

AR²Net: An Attentive Neural Approach for Business Location Selection with Satellite Data and Urban Data
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Business intelligence

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 2

April 2020

322 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3382774

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 February 2020

Accepted: 01 November 2019

Revised: 01 September 2019

Received: 01 January 2019

Published in TKDD Volume 14, Issue 2

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2030 National Key AI Program of China
National Science Foundation of China
Innovation and Entrepreneurship Foundation for oversea high-level talents of Shenzhen
NSFC
Shanghai Municipal Science and Technology Commission
Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, the Program for Shanghai Top Young Talents, Shanghai Engineering Research Center of Digital Education Equipment
SJTU Global Strategic Partnership Fund

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Cited By

Gemar GPérez-Benítez V(2024)Review of Business Location Research: a Bibliometric Analysis from 1968 to 2022Revista CENTRA de Ciencias Sociales10.54790/rccs.923:2Online publication date: 3-Jul-2024
https://doi.org/10.54790/rccs.92
Liu YWang WWang HAlidaee B(2023)Quantum Machine Learning on Remote Sensing Data ClassificationJournal of Engineering Research and Sciences10.55708/js02120042:12(23-33)Online publication date: Dec-2023
https://doi.org/10.55708/js0212004
Li TXi YWang HLi YTarkoma SHui P(2023)Learning Representations of Satellite Imagery by Leveraging Point-of-InterestsACM Transactions on Intelligent Systems and Technology10.1145/358934414:4(1-32)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3589344
Zhu QChen YWang HZeng ZLiu H(2022)A Knowledge-Enhanced Framework for Imitative Transportation Trajectory Generation2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00093(823-832)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00093
Yan HWang SYang YGuo BHe TZhang D(2022)$O^{2}$-SiteRec: Store Site Recommendation under the O2O Model via Multi-graph Attention Networks2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00044(525-538)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00044
Gao YHan DWang HZhang MHou FYu DXiong Y(2022)GeoGTI: Towards a General, Transferable and Interpretable Site RecommendationWeb Information Systems and Applications10.1007/978-3-031-20309-1_49(559-571)Online publication date: 8-Dec-2022
https://doi.org/10.1007/978-3-031-20309-1_49
Liu YGuo BZhang DZeghlache DChen JZhang SZhou DShi XYu Z(2021)MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge TransferACM Transactions on Intelligent Systems and Technology10.1145/344727112:3(1-23)Online publication date: 22-Apr-2021
https://doi.org/10.1145/3447271
Machii KChiba KKawakatsu Y(2021)Feedback-circulating Optimum Design for Perceiving Constellation Principle of Regional Observation Satellites2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9660141(1-8)Online publication date: 5-Dec-2021
https://doi.org/10.1109/SSCI50451.2021.9660141

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