Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia.
Big Data. 2020 Dec;8(6):519-527. doi: 10.1089/big.2020.0028.
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.
给定一个特定的感兴趣的位置，推荐一个零售业务并不简单。这种推荐过程需要仔细研究人口统计数据、商圈特征、销售业绩、流量和环境特征。这不仅需要耗费大量的人力，而且由于专家意见的主观性，往往会导致不一致。当无法使用销售数据来进行推荐时，这个过程就变得更加具有挑战性。作为克服这些挑战的一种尝试，本研究使用了机器学习方法，该方法利用相似度度量来进行推荐。然而，在使用机器学习方法时，有两个挑战需要特别注意：（1）如何准备一个特征集，该特征集可以普遍代表不同类型的零售业务；（2）哪种相似度度量方法可以产生最佳的推荐准确性？本研究使用的数据集包括兴趣点、人口、房地产、工作类型和教育水平。进行了实证研究，以调查（1）提出的相似性度量方法对零售业务推荐的总体准确性；（2）所提出的方法是否对某些零售类别存在偏见。总之，研究结果表明，所提出的基于相似度的技术可以产生高于 70%的准确性，并且在推荐类似的一组零售业务时，准确性更高。
