Title: Collaborative filtering recommendation algorithm for e-commerce products based on Bayesian network

Authors: Hongli Wan

Addresses: School of Computer, Dalian Neusoft University of Information, Dalian, 116023, China

Abstract: In order to effectively improve the recommendation accuracy of collaborative screening of e-commerce products, ensure the results of collaborative screening of e-commerce products, and reduce the time consumption of collaborative screening of e-commerce products, this project plans to study the recommendation algorithm for collaborative screening of e-commerce products based on Bayesian networks. Use an identical method to determine the user selection of e-commerce platform. On this basis, a new method was adopted to conduct an overall similarity analysis of products in e-commerce platforms. Then, using Bayesian networks and expected maximum methods, establish a model of user interest in e-commerce products on e-commerce platforms, thereby completing joint screening of e-commerce products. Through testing the joint screening of different types of e-commerce products, it was found that this method had a recommendation rate of 91.6% and an accuracy rate of 95.9% in the joint screening of e-commerce products.

Keywords: Bayesian network; e-commerce products; expectation maximisation algorithm; collaborative filtering recommendation; slope one algorithm.

DOI: 10.1504/IJRIS.2024.142357

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.4, pp.331 - 336

Received: 30 Dec 2022
Accepted: 21 Mar 2023
Published online: 25 Oct 2024 *

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