Title: Study on personalised search of English teaching resources database based on semantic association mining

Authors: Xiujuan Wang; Tao Wei

Addresses: College of Quality Education, Hunan Railway Professional Technology College, Zhuzhou, Hunan, China ' School of Basic Courses, Hunan Judicial Police Vocational College, Changsha, Hunan, China

Abstract: To address the issue of low recall and accuracy in personalised retrieval of English teaching resource databases, a personalised retrieval method based on semantic association mining is proposed. Firstly, data analysis is conducted on the resources in the English teaching resource library to classify them, extract semantic features of the resources and then, with user learning duration, user learning frequency, user learning motivation and the proportion of detailed usage of viewing words as inputs, a user interest model is constructed and solved to output the user's learning style. Finally, based on the user's interest model and the semantic features of the resource library text, the most relevant keywords to the user's interest are determined, and personalised retrieval of English teaching resource database information based on the input keywords is completed. The experimental results show that the personalised retrieval accuracy of the English teaching resource library using this method is higher than 98.2%, and the highest recall rate can reach 99.6%. This indicates that the application of this method can effectively improve the personalised retrieval effect of the English teaching resource library.

Keywords: semantic association mining; genetic algorithm; differential privacy; user interest model; personalised search.

DOI: 10.1504/IJCAT.2023.138829

International Journal of Computer Applications in Technology, 2023 Vol.73 No.4, pp.253 - 260

Received: 21 Sep 2023
Accepted: 15 Dec 2023
Published online: 31 May 2024 *

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