TY - GEN
T1 - A Convolutional Approach for Estimating Popularity of New Branch Stores
AU - Lin, Fandel
AU - Hsieh, Hsun Ping
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85091702322&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091702322&partnerID=8YFLogxK
U2 - 10.1145/3366424.3382709
DO - 10.1145/3366424.3382709
M3 - Conference contribution
AN - SCOPUS:85091702322
T3 - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
SP - 85
EP - 87
BT - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -