On Enhancing E-Commerce Shipping Policies with Blockchain and Recommender Systems

被引:0
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作者
Suneel Kumar [1 ]
Sarvesh Pandey [2 ]
Umesh Bhatt [2 ]
机构
[1] Banaras Hindu University,Department of Computer Science
[2] MMV,Computer Science
[3] Banaras Hindu University,undefined
关键词
Blockchain; Intelligent shipping; Recommendation system; TPC-H;
D O I
10.1007/s42979-025-03687-x
中图分类号
学科分类号
摘要
E-commerce systems aim to deliver products on time and at a competitive price to customers through the Internet. Though transformational, adapting to an internet-based online system led to higher shipping charges (being borne by customers) and overwhelming options (requiring endless time to make purchasing decisions). Most existing shipping policies exempt the shipping fee only when the customer’s order value exceeds a pre-set threshold; they do not consider the frequency of orders made by a customer when deciding on a shipping fee exemption. First, to address the shipping charge problem, we propose a History Informed Shipping (HIShip) method, which utilizes the customer’s transaction history in making the shipping charge exemption decisions. HIShip mainly benefits low (and mid) order-value customers who frequently order and sellers with a product cost lower than the pre-defined shipping exemption threshold amount. The greater customer and seller participation eventually contribute to higher revenue from the e-commerce platform. Furthermore, we store order history in the blockchain to ensure decentralization and immutability in a trustless environment. HIShip’s shipping policy is evaluated against a naive threshold-based shipping policy on the TPC-H dataset, and results confirm that 21.5% and 21.06% increase in the percentage of orders (placed by low and mid ’order-value’ customers, respectively) qualify for the shipping fee exemption. Second, we integrated an ML-based recommendation mechanism to suggest appropriate product(s) further in case the actual order does not qualify for shipping fee exemption. Evaluation results show that SVD is the best model with a minimum RMSE of 0.765364 and MAE of 0.508519 on the ‘All Beauty’ dataset.
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