金矿在哪里？通过Facebook数据分析寻找有前途的商业地点
Where is the Goldmine? Finding Promising Business Locations through Facebook Data Analytics
摘要：
如果你要开自己的咖啡馆，你不想毫不费力地找到最合适的位置来开店吗？选择一个最佳的物理位置是许多企业的关键决定，因为许多因素有助于最终选择的位置。在本文中，我们试图通过调查使用公开的Facebook页面数据来解决这个问题-其中包括用户签到，业务类型和业务位置-来评估用户选择的物理位置相对于一种类型的业务。使用新加坡20，877家食品企业的数据集，我们对几个关键因素进行了分析，包括企业类别，位置和邻近企业。从这些因素中，我们提取了一组相关的功能，并开发了一个强大的预测模型来估计一个商业地点的受欢迎程度。我们的实验表明，邻近业务的流行程度有助于关键特征执行准确的预测。最后，我们通过一个交互式的Web应用系统说明了我们所提出的方法的实际用途。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
