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Geospatial Insights for Retail Recommendation Using Similarity Measures.

Choo-Yee Ting1, Chiung Ching Ho1, Hui-Jia Yee1

  • 1Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia.

Big Data
|December 21, 2020
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Summary

This study introduces a machine learning approach for retail business recommendations using similarity measures. The method achieves over 70% accuracy, especially for similar business types, overcoming challenges without sales data.

Keywords:
geospatial insightsretail recommendationsimilarity measures

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Area of Science:

  • Geographic Information Systems (GIS)
  • Retail Analytics
  • Machine Learning

Background:

  • Recommending retail businesses based on location is complex, involving demographics, trade area characteristics, sales, traffic, and environmental factors.
  • Manual recommendation processes are labor-intensive and prone to inconsistency due to subjective expert opinions.
  • Challenges arise when sales data is unavailable for making informed recommendations.

Purpose of the Study:

  • To develop and evaluate a machine learning approach using similarity measures for retail business recommendations.
  • To address the challenges of feature set preparation for diverse retail types and selecting optimal similarity measures.
  • To assess the accuracy and potential biases of proposed recommendation approaches across different retail categories.

Main Methods:

  • Utilized a machine learning approach centered on similarity measures for retail business recommendation.
  • Developed a common feature set incorporating points of interest, population, property, job type, and education level data.
  • Conducted empirical studies to evaluate recommendation accuracy and category bias.

Main Results:

  • Proposed similarity-based techniques achieved recommendation accuracy exceeding 70%.
  • Higher accuracy was observed when recommendations were made within a cluster of similar retail businesses.
  • Empirical studies indicated no significant bias toward specific retail categories.

Conclusions:

  • Machine learning, particularly similarity measures, offers a viable solution for retail business recommendation, especially when sales data is absent.
  • The developed feature set and similarity approaches provide a robust framework for location-based retail recommendations.
  • The findings support the effectiveness of data-driven, similarity-based methods in enhancing the accuracy and consistency of retail recommendations.