Collaborative filtering recommendation algorithm for e-commerce products based on Bayesian network Online publication date: Fri, 25-Oct-2024
by Hongli Wan
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 16, No. 4, 2024
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.
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