Location-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.
The industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.
The coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.
This study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.
An industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.
