Title: Study on personalised recommendation method of English online learning resources based on improved collaborative filtering algorithm

Authors: Xiuqin Zhang; Jigang Xie

Addresses: School of Innovation and Entrepreneurship, Nanjing Vocational University of Industry Technology, Nanjing, 210023, China ' School of Business and Trade, Nanjing Vocational University of Industry Technology, Nanjing, 210023, China

Abstract: In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

Keywords: improve collaborative filtering; English learning resources; personalised recommendation; matrix decomposition; K-means clustering; heuristic clustering.

DOI: 10.1504/IJBIDM.2024.140879

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.362 - 381

Received: 16 Aug 2023
Accepted: 16 Nov 2023
Published online: 03 Sep 2024 *

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