Title: A personalised recommendation method of online and offline mixed teaching resources based on user preference behaviour

Authors: Qingqing He

Addresses: School of Public Administration, Chongqing Public Transport Vocational College, Chongqing 402260, China

Abstract: For online and offline hybrid teaching resources, due to the low recommendation accuracy and long recommendation time of traditional personalised recommendation methods, a personalised recommendation method based on user preference behaviour is proposed. First, we collect the teaching resource data through the crawler technology, then clean the obtained data, and then build the teaching resource model. Finally, we build the user model, calculate the interest preference behaviour group category that the user belongs to, determine the user preference behaviour, and use cosine similarity to measure the similarity between users, so as to predict the user score, and recommend the resource with the highest score to the user. The experimental results show that the proposed method has higher accuracy and shorter recommendation time.

Keywords: user preference behaviour; teaching; personalised recommendation; crawler technology; cosine similarity.

DOI: 10.1504/IJRIS.2024.143162

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.5, pp.345 - 351

Received: 06 Dec 2022
Accepted: 14 Mar 2023
Published online: 05 Dec 2024 *

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