Title: Research on user personalised product recommendation method of e-commerce platform based on rough set

Authors: Xin Yao; Xiaowei Ma; Shizhong Guo

Addresses: Department of Information Engineering, Hebei Energy College of Vocation and Technology, Tangshan, 063000, China ' Department of Economics and Management, Hebei Energy College of Vocation and Technology, Tangshan, 063000, China ' Department of Transportation, Tangshan Maritime Vocational College, Tangshan, 063000, China

Abstract: To improve the purchase rate and recommendation accuracy of personalised product recommendation methods, this paper proposes the research of personalised product recommendation methods for e-commerce platform users based on rough sets. First, extract the recommended product features, and use naive Bayesian method to classify the emotion of product features. Secondly, based on rough set theory, the similarity of product recommendation is calculated and the product similarity is arranged. Then, build the scoring matrix to design the product recommendation function. Finally, the product popularity is introduced to score and predict the target users, so as to generate the final Top-N product list and realise personalised product recommendation for users. The results show that the purchase rate of recommended products users in this method is between 68.3% and 88.0%, and the accuracy of product recommendation can reach 0.989, effectively improving the effect of personalised product recommendation.

Keywords: rough set; similarity; weighted summation; Naive Bayes; commodity popularity.

DOI: 10.1504/IJRIS.2024.143155

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.5, pp.408 - 416

Received: 31 Jan 2023
Accepted: 21 Mar 2023
Published online: 05 Dec 2024 *

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