Publication Type

Conference Proceeding Article

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.

Discipline

Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

HT '16 Proceedings of the 27th ACM Conference on Hypertext and Social Media

First Page

93

Last Page

102

ISBN

978-1-4503-4247-6

Identifier

10.1145/2914586.2914588

Publisher

ACM

City or Country

NY, USA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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