A Convolutional Approach for Estimating Popularity of New Branch Stores

Fandel Lin, Hsun Ping Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Thousands of shops are opened and closed down constantly every day. Determining the location of a new branch store is a critical issue for retail business. Making decisions for locations of new stores has been widely studied in many fields considering cost saving and profits. In this study, we propose the "Grid-liked Graph" (GLG), a graph-based approach for spatial data division to model the spatial distribution of urban features. Based on the proposed GLG, four footprint features are extracted from location-based social network data and treated as image features. Convolution Neural Network (CNN)-based models including LeNet and AlexNet are then adopted to infer the popularity of each candidate store for optimizing location selection. According to our experimental results on two kinds of retail stores, the proposed methods can increase usage of data, and can also perform well for datasets with fewer features compared with previous studies.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Pages85-87
Number of pages3
ISBN (Electronic)9781450370240
DOIs
Publication statusPublished - 2020 Apr 20
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan
Duration: 2020 Apr 202020 Apr 24

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan
CityTaipei
Period20-04-2020-04-24

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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