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

科研成果: 书/报告/会议事项章节会议稿件同行评审

14 引用 (Scopus)

摘要

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.

源语言英语
主期刊名UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
出版商Association for Computing Machinery, Inc
149-152
页数4
ISBN(电子版)9781450344623
DOI
出版状态已出版 - 12 9月 2016
活动2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, 德国
期限: 12 9月 201616 9月 2016

出版系列

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

会议

会议2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
国家/地区德国
Heidelberg
时期12/09/1616/09/16

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