Title: An online teaching resource recommendation algorithm based on category similarity

Authors: Lingyu Chen

Addresses: Teaching Research Department, Weifang University of Science and Technology, Shouguang, 262700, Shandong, China

Abstract: To overcome the problems of poor recall rate, time-consuming resource recommendation list generation, and low ideal loss cumulative gain in traditional online teaching resource recommendation algorithms, a category similarity based online teaching resource recommendation algorithm is proposed. Firstly, segment the user group of online teaching resources and construct a learning user profile of online teaching resources based on dynamic field theory. Secondly, the similarity between user categories and teaching categories is obtained, and the similarity of online teaching resource categories is obtained through sorting. Finally, based on category similarity, an online teaching resource PAF recommender is constructed, taking into account teaching popularity and user loyalty to achieve online teaching resource recommendation. The experimental results show that the online teaching resource recommendation recall rate of this algorithm can reach 99.9%, the ideal cumulative loss gain of teaching resource recommendation is 66.18, and the resource recommendation list generation time is 11 s.

Keywords: category similarity; teaching resources; recommendation algorithm; dynamics; product adoption forecasting; PAF recommender.

DOI: 10.1504/IJRIS.2025.148716

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.5, pp.342 - 349

Received: 05 Jun 2023
Accepted: 31 Jul 2023
Published online: 21 Sep 2025 *

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