Title: A personalised push method of English mobile reading resources based on tag similarity

Authors: Juanzhi Shi; Yadan Deng

Addresses: Department of Foreign Language, Xiangnan University, Chenzhou 423000, Hunan, China ' Department of Foreign Language, Xiangnan University, Chenzhou 423000, Hunan, China

Abstract: Aiming at shortening the recommendation time of reading resources and improving the retention rate of recommendation results, a new personalised push method of English mobile reading resources was studied based on label similarity. Firstly, preprocess resource data by data cleaning and noise reduction. K-means clustering algorithm is used to cluster resource data. Secondly, calculate the frequency of resource tags used by users according to their implicit needs and preferences. Finally, the cosine similarity between user tags and resource tags in the same category is obtained, and a push list is formed to push resources before ranking test for users. The experimental results show that the maximum generation time of the push list is only 4,372 ms after the application of this method, and the retention rate of the recommendation results is always above 93%.

Keywords: tag similarity; user tag; resource tag; English mobile reading resources; personalised push; K-means clustering; push list.

DOI: 10.1504/IJBIDM.2024.137739

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.3/4, pp.266 - 277

Received: 28 Nov 2022
Accepted: 07 Mar 2023
Published online: 04 Apr 2024 *

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