Title: Digital education mining technology based on composite collaborative filtering and Eclat algorithm

Authors: Jingya Wang; Qi Han; Kunkun Ma; Li Xu

Addresses: Yantai Vocational College, Yantai, 264670, China ' Organization and Personnel Department, Yantai Vocational College, Yantai, 264670, China ' College of Humanities and Communication, Shandong Technology and Business University, Yantai, 264005, China ' Basic Teaching Department, Yantai Vocational College, Yantai, 264670, China

Abstract: The field of digital education is rapidly growing, demanding effective resource utilisation. Traditional collaborative filtering (CF) algorithms face challenges with large, complex datasets. This study addresses these limitations by integrating CF with association rule mining, using a novel IBCF-UBCF composite CF algorithm and Eclat technology. Data was collected from multiple sources and fused for enhanced educational mining. Results show Eclat outperforms apriori, reducing CPU usage by 55% and physical memory usage by 51.9%, while the composite filtering algorithm achieved over 99% accuracy. The Eclat-IBCF-UBCF algorithm offers robust support for digital education, advancing educational data mining and personalised recommendations. It is recommended for implementation in digital education systems due to its efficiency and accuracy. Further research should focus on enhancing and integrating this algorithm with other educational technologies.

Keywords: Eclat; IBCF algorithm; UBCF algorithm; composite collaborative filtering; digital education.

DOI: 10.1504/IJWET.2025.146734

International Journal of Web Engineering and Technology, 2025 Vol.20 No.2, pp.122 - 145

Received: 19 Mar 2024
Accepted: 14 Nov 2024
Published online: 16 Jun 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article