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

Jing Li; Bin Guo; Zhu Wang; Mingyang Li; Zhiwen Yu

doi:10.1145/2968219.2971405

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

Jing Li, Bin Guo, Zhu Wang, Mingyang Li, Zhiwen Yu

School of Computer Science

Northwestern Polytechnical University Xian

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

14 Scopus citations

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 crossspace 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.

Original language	English
Title of host publication	UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Publisher	Association for Computing Machinery, Inc
Pages	149-152
Number of pages	4
ISBN (Electronic)	9781450344623
DOIs	https://doi.org/10.1145/2968219.2971405
State	Published - 12 Sep 2016
Event	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany Duration: 12 Sep 2016 → 16 Sep 2016

Publication series

Name	UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Conference

Conference	2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
Country/Territory	Germany
City	Heidelberg
Period	12/09/16 → 16/09/16

Keywords

Chain Store Recommendation
Cross-Space
Multi-Scale Classification
Urban Data

Access to Document

10.1145/2968219.2971405

Cite this

Li, J., Guo, B., Wang, Z., Li, M., & Yu, Z. (2016). Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation. In UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 149-152). (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2968219.2971405

Li, Jing ; Guo, Bin ; Wang, Zhu et al. / Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. pp. 149-152 (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

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title = "Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation",

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 crossspace 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.",

keywords = "Chain Store Recommendation, Cross-Space, Multi-Scale Classification, Urban Data",

author = "Jing Li and Bin Guo and Zhu Wang and Mingyang Li and Zhiwen Yu",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 ; Conference date: 12-09-2016 Through 16-09-2016",

year = "2016",

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doi = "10.1145/2968219.2971405",

language = "英语",

series = "UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing",

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Li, J, Guo, B , Wang, Z, Li, M & Yu, Z 2016, Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation. in UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Inc, pp. 149-152, 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, Heidelberg, Germany, 12/09/16. https://doi.org/10.1145/2968219.2971405

Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation. / Li, Jing; Guo, Bin ; Wang, Zhu et al.
UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2016. p. 149-152 (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

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

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AU - Guo, Bin

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PY - 2016/9/12

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N2 - 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 crossspace 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.

AB - 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 crossspace 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.

KW - Chain Store Recommendation

KW - Cross-Space

KW - Multi-Scale Classification

KW - Urban Data

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PB - Association for Computing Machinery, Inc

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Li J, Guo B , Wang Z, Li M, Yu Z. Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation. In UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc. 2016. p. 149-152. (UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing). doi: 10.1145/2968219.2971405

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

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Keywords

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