Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2016

Abstract

If you were to open your own cafe, would you not want to effortlessly identify the most suitable location to set up your shop? Choosing an optimal physical location is a critical decision for numerous businesses, as many factors contribute to the final choice of the location. In this paper, we seek to address the issue by investigating the use of publicly available Facebook Pages data-which include user "check-ins", types of business, and business locations-to evaluate a user-selected physical location with respect to a type of business. Using a dataset of 20,877 food businesses in Singapore, we conduct analysis of several key factors including business categories, locations, and neighboring businesses. From these factors, we extract a set of relevant features and develop a robust predictive model to estimate the popularity of a business location. Our experiments have shown that the popularity of neighboring business contributes the key features to perform accurate prediction. We finally illustrate the practical usage of our proposed approach via an interactive web application system.

Keywords

Location analytics, Facebook, feature extraction, machine learning

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media: Halifax, Canada, July 10-13

First Page

93

Last Page

102

ISBN

9781450342476

Identifier

10.1145/2914586.2914588

Publisher

ACM

City or Country

New York

Copyright Owner and License

LARC

Additional URL

https://doi.org/10.1145/2914586.2914588

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