Geospatial Insights for Retail Recommendation Using Similarity Measures


Ting, Choo Yee and Yee, Hui Jia and Ho, Chiung Ching (2020) Geospatial Insights for Retail Recommendation Using Similarity Measures. Big Data, 8 (6). pp. 519-527. ISSN 2167-6461

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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.

Item Type: Article
Uncontrolled Keywords: Retail business
Subjects: H Social Sciences > HF Commerce > HF5001-6182 Business > HF5428-5429.6 Retail trade
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 26 Oct 2021 01:46
Last Modified: 26 Oct 2021 01:46


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