Title: Adaptive recommendation method for teaching resources based on knowledge graph and user similarity

Authors: Meng Li

Addresses: Department of Financial Management, Xi'an International University, Xi'an, 710077, China

Abstract: To provide users with personalised and accurate teaching resource recommendation results, a new teaching resource adaptive recommendation method is proposed by effectively integrating knowledge graph with user similarity. This method first constructs a knowledge graph of teaching resources, representing the relationship between resources as a graph structure. Then, by analysing user learning history, ratings, and preferences, calculate user similarity and identify other users with higher similarity to the current user. Next, based on the resource ratings between similar users and current users, combined with the resource association relationship in the knowledge graph, the resource ratings are calculated using methods such as weighted summation. Finally, teaching resources are sorted based on resource ratings and recommended to current users. The experimental results show that the maximum root mean square error of this method is only 0.26, the highest recall rate is 95.6% and the MRR value is relatively high.

Keywords: knowledge graph; user similarity; ICA algorithm; improve collaborative filtering algorithms; preference learning algorithm.

DOI: 10.1504/IJBIDM.2025.143931

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.1/2, pp.88 - 99

Received: 31 Oct 2023
Accepted: 07 May 2024
Published online: 14 Jan 2025 *

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