Title: Personalised recommendation method of Japanese teaching resources based on collaborative filtering

Authors: Pan Zhao; Yang Wang

Addresses: College of Oriental Language, Harbin Normal University, Harbin, 150000, China ' College of Oriental Language, Harbin Normal University, Harbin, 150000, China

Abstract: In the existing personalised recommendation methods of Japanese teaching resources, the confidence of resource recommendation is low, and the personalised recommendation effect is poor. Therefore, a personalised recommendation method for Japanese teaching resources based on collaborative filtering is designed. Firstly, the multi-modal interest similarity model and hidden factor depth are used to extract the features of Japanese teaching resources. Then, the scoring matrix and label matrix are constructed, and TF-IDF algorithm is introduced to calculate user preferences, so as to realise user preference mining of Japanese teaching resources. Finally, the Japanese teaching resources recommendation scoring matrix is constructed, similar neighbours with preferences are sought, and personalised recommendation rules are set to realise personalised recommendation of Japanese teaching resources. The experimental results show that the proposed method can improve the confidence of Japanese teaching resource recommendation and improve the poor performance of personalised recommendation.

Keywords: collaborative filtering; Japanese language teaching resources; preference; scoring matrix; label matrix; penalty factor.

DOI: 10.1504/IJRIS.2025.149715

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.6, pp.355 - 363

Received: 05 Jun 2023
Accepted: 14 Jul 2023
Published online: 11 Nov 2025 *

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