Recommending a retail business given a particular location of interest is nontrivial. Such a recommendation process requires careful study of demographics, trade area characteristics, sales performance, traffic, and environmental features. It is not only human effort taxing but often introduces inconsistency due to subjectivity in expert opinions. The process becomes more challenging when no sales data can be used to make a recommendation. As an attempt to overcome the challenges, this study used the machine learning approach that utilizes similarity measures to perform the recommendation. However, two challenges required careful attention when using the machine learning approach: (1) how to prepare a feature set that can commonly represent different types of retail business and (2) which similarity measure approach produces optimal recommendation accuracy? The data sets used in this study consist of points of interest, population, property, job type, and education level. Empirical studies were conducted to investigate (1) the overall accuracy of proposed similarity measure approaches to the retail business recommendation, and (2) whether the proposed approaches have a bias toward certain retail categories. In summary, the findings suggested that the proposed similarity-based techniques elicited an accuracy of above 70% and demonstrated higher accuracy when the recommendation was made within a set of similar retail businesses.

中文翻译：

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使用相似性度量的零售推荐地理空间洞察

推荐给定感兴趣的特定位置的零售业务并非易事。这样的推荐过程需要仔细研究人口统计、贸易区特征、销售业绩、交通和环境特征。这不仅会耗费人力，而且由于专家意见的主观性，常常会导致不一致。当没有销售数据可用于提出建议时，该过程变得更具挑战性。为了克服这些挑战，本研究使用了机器学习方法，该方法利用相似性度量来执行推荐。但是，在使用机器学习方法时，需要仔细注意两个挑战：(1) 如何准备一个可以共同代表不同类型零售业务的特征集，以及 (2) 哪种相似性度量方法可以产生最佳推荐精度？本研究中使用的数据集包括兴趣点、人口、财产、工作类型和教育水平。进行了实证研究以调查 (1) 建议的零售业务推荐相似性度量方法的整体准确性，以及 (2) 建议的方法是否对某些零售类别有偏见。总之，研究结果表明，所提出的基于相似性的技术的准确率超过 70%，并且当在一组类似的零售业务中提出推荐时，其准确率更高。工作类型和教育水平。进行了实证研究以调查 (1) 建议的零售业务推荐相似性度量方法的整体准确性，以及 (2) 建议的方法是否对某些零售类别有偏见。总之，研究结果表明，所提出的基于相似性的技术的准确率超过 70%，并且当在一组类似的零售业务中提出推荐时，其准确率更高。工作类型和教育水平。进行了实证研究以调查 (1) 建议的零售业务推荐相似性度量方法的整体准确性，以及 (2) 建议的方法是否对某些零售类别有偏见。总之，研究结果表明，所提出的基于相似性的技术的准确率超过 70%，并且当在一组类似的零售业务中提出推荐时，其准确率更高。