poster

Where to place the next outlet? harnessing cross-space urban data for multi-scale chain store recommendation

Authors:

Jing Li,

Bin Guo,

Zhu Wang,

Mingyang Li,

Zhiwen YuAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Pages 149 - 152

https://doi.org/10.1145/2968219.2971405

Published: 12 September 2016 Publication History

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Abstract

Chain store has become an important business form in modern society. For the same chain group, they often have inner store classification regarding the store scale to meet the service requests and profit optimization needs of different areas. In this paper, we present ChainRec, a framework for chain store placement recommendation considering its scale. Specifically, we extract three types of associative features from cross-space data sources, including geographic features, commercial features, as well as scale features. Based on these features, we adopt supervised regression and classification to solve two scale-specific chain store placement problems. Experiments with online and offline datasets from the Chengdu City in China validate the effectiveness of our framework.

References

[1]

Karamshuk, D., et al. 2015. Geo-spotting: mining online location-based services for optimal retail store placement. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'13), 793--801.

Digital Library

Google Scholar

[2]

Bin, G., et al. 2016. Mobile Crowd Sensing and Computing: When Participatory Sensing Meets Participatory Social Media. IEEE Communications Magazine, 54, 2: 131--137.

Digital Library

Google Scholar

[3]

Chen, L., et al. 2015. Bike sharing station placement leveraging heterogeneous urban open data. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'15), 571--575.

Digital Library

Google Scholar

[4]

Tian, M., et al. 2015. Combining social media and location-based services for shop type recommendation. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 161--164.

Digital Library

Google Scholar

[5]

Yuan, J., et al. 2012. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'12), 186--194.

Digital Library

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Li PChang YWei MHsieh H(2024)Estimating Future Financial Development of Urban Areas for Deploying Bank Branches: A Local-Regional Interpretable ModelACM Transactions on Management Information Systems10.1145/365647915:2(1-26)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3656479
Hsieh HLai BChen YChang Y(2023)Multi-Performance Estimation for Deploying Bank Branches Based on a Multi-Task Attentive Tree-Enhanced ModelIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.32145767:1(237-249)Online publication date: Feb-2023
https://doi.org/10.1109/TETCI.2022.3214576
Wang ZWang ZLi XYu ZGuo BChen LZhou X(2023)Exploring Multi-Dimension User-Item Interactions With Attentional Knowledge Graph Neural Networks for RecommendationIEEE Transactions on Big Data10.1109/TBDATA.2022.31547789:1(212-226)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3154778
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Index Terms

Where to place the next outlet? harnessing cross-space urban data for multi-scale chain store recommendation
1. Human-centered computing

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Information & Contributors

Information

Published In

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

September 2016

1807 pages

ISBN:9781450344623

DOI:10.1145/2968219

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2016

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UbiComp '16

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UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Li PChang YWei MHsieh H(2024)Estimating Future Financial Development of Urban Areas for Deploying Bank Branches: A Local-Regional Interpretable ModelACM Transactions on Management Information Systems10.1145/365647915:2(1-26)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3656479
Hsieh HLai BChen YChang Y(2023)Multi-Performance Estimation for Deploying Bank Branches Based on a Multi-Task Attentive Tree-Enhanced ModelIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2022.32145767:1(237-249)Online publication date: Feb-2023
https://doi.org/10.1109/TETCI.2022.3214576
Wang ZWang ZLi XYu ZGuo BChen LZhou X(2023)Exploring Multi-Dimension User-Item Interactions With Attentional Knowledge Graph Neural Networks for RecommendationIEEE Transactions on Big Data10.1109/TBDATA.2022.31547789:1(212-226)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TBDATA.2022.3154778
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: 16-Sep-2022
https://dl.acm.org/doi/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: 21-Apr-2021
https://dl.acm.org/doi/10.1145/3447271
Liu YGuo BZhang DZeghlache DChen JHu KZhang SZhou DYu Z(2021)Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site RecommendationACM Transactions on Knowledge Discovery from Data10.1145/344220315:3(1-27)Online publication date: 21-Apr-2021
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Gao CHuang CYu YWang HLi YJin D(2019)Privacy-preserving Cross-domain Location RecommendationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33143983:1(1-21)Online publication date: 29-Mar-2019
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Liu YGuo BLi NZhang JChen JZhang DLiu YYu ZZhang SYao L(2019)DeepStore: An Interaction-Aware Wide&Deep Model for Store Site Recommendation With Attentional Spatial EmbeddingsIEEE Internet of Things Journal10.1109/JIOT.2019.29161436:4(7319-7333)Online publication date: Aug-2019
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Xavier AMenezes ANakamura FFigueiredo CNeto MNovais RFerraz CViana W(2018)Analysis of Urban Regions Popularity Using FoursquareProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243119(379-386)Online publication date: 16-Oct-2018
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