Improving a Recommendation Engine for Traditional Trade Between Wholesalers and Retailers Using Association Rules
K. Chugh, Nantachai Kantanantha · 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) · 2022
This paper explores the data collection and mining of association rules to generate over 8,500 association rules which improve an existing recommendation engine used in an eCommerce platform between traditional trade wholesalers and traditional trade retailers. This improved recommendation engine allows traditional trade retailers to receive personalized recommendations based on items in their cart, and improves the current recommendation engine which only recommends most sold products. The improved recommendation engine helps traditional trade retailers purchase the right products for their stores and allows traditional trade wholesalers to increase the revenue of their stores, thereby providing both traditional trade wholesalers and traditional trade retailers with tools to help compete against modern trade outlets.