Authors: Dan Li
Addresses: Ideological and Political Theory Teaching Department, Henan Industry and Trade Vocational College, Zhengzhou, 450000, China
Abstract: The research uses Takagi-Sugeno (T-S) fuzzy control combined with neural matrix factorization (Neu MF) model to study the intelligent recommendation of educational resources. The recommendation performance of TS-Neu MF model is compared with other similar recommendation algorithm models under two test sets of E's dx and C er. The results of the experiments show that the TS-Neu MF model outperforms Deep FM by 56.6% in root mean square error (RMSE) metrics and 71.5% in mean absolute error (MAE) metrics, and outperforms the Neu MF model by 33.1% in RMSE metrics and 22.5% in MAE metrics under the E dx dataset. The training loss is about 0.04 lower than the Deep FM model, about 0.006 lower than the BPNN model, and about 0.02 lower than the Neu MF model.
Keywords: Neu MF; T-S fuzzy; educational resources; intelligent recommendation.
International Journal of Knowledge-Based Development, 2023 Vol.13 No.1, pp.94 - 111
Received: 25 Oct 2022
Accepted: 07 Dec 2022
Published online: 06 Apr 2023 *