Title: Enhancing e-commerce product recommendations through statistical settings and product-specific insights

Authors: Onur Dogan

Addresses: Department of Management Information Systems, Izmir Bakircay University, 35665, Izmir, Turkey; Department of Mathematics, University of Padua, Padua, 35122, Italy

Abstract: In the e-commerce industry, effectively guiding customers to select desired products poses a significant challenge, necessitating the utilisation of technology and data-driven solutions. To address the extensive range of product varieties and enhance product recommendations, this study improves upon the conventional association rule mining (ARM) approach by incorporating statistical settings. By examining sales transactions, the study assesses the statistical significance of correlations, taking into account specific product details such as product name, discount rates, and the number of favourites. The findings offer valuable insights with managerial implications. For instance, the study recommends that if a customer adds products with a high discount rate to their basket, the company should suggest products with a lower discount rate. Furthermore, the traditional rules are augmented by incorporating product features. Specifically, when the total number of favourites is below 7,500 and the discount rate is less than 75%, the similarity ratio of the recommended products should be below 0.50. These enhancements contribute significantly to the field, providing actionable recommendations for e-commerce companies to optimise their product recommendation strategies.

Keywords: association rules; basket analysis; statistical tests; e-commerce.

DOI: 10.1504/IJCSE.2024.142831

International Journal of Computational Science and Engineering, 2024 Vol.27 No.6, pp.643 - 653

Received: 27 Nov 2022
Accepted: 16 Jul 2023
Published online: 28 Nov 2024 *

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