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

Published: 12 September 2016 Publication History

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.
[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.
[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.
[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.
[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.

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

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    Published In

    cover image ACM Conferences
    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
    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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    New York, NY, United States

    Publication History

    Published: 12 September 2016

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    Author Tags

    1. chain store recommendation
    2. cross-space
    3. multi-scale classification
    4. urban data

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

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    Cited By

    View all
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (2018)CityTransferProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31614111:4(1-23)Online publication date: 8-Jan-2018
    • Show More Cited By

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