A Convolutional Approach for Estimating Popularity of New Branch Stores

Fandel Lin, Hsun Ping Hsieh

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
發行者Association for Computing Machinery
頁面85-87
頁數3
ISBN(電子)9781450370240
DOIs
出版狀態Published - 2020 四月 20
事件29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan
持續時間: 2020 四月 202020 四月 24

出版系列

名字The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
國家Taiwan
城市Taipei
期間20-04-2020-04-24

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

指紋 深入研究「A Convolutional Approach for Estimating Popularity of New Branch Stores」主題。共同形成了獨特的指紋。

引用此