ICRA: A study of highly accurate course recommendation models incorporating false review filtering and ERNIE 3.0.

Autor: Li B; School of Software, Jiangxi Normal University, Nanchang, Jiangxi, China.; Research Centre for Management Science and Engineering, Jiangxi Normal University, Nanchang, Jiangxi, China.; Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance, Nanchang, Jiangxi, China., Hou Y; School of Software, Jiangxi Normal University, Nanchang, Jiangxi, China.; Research Centre for Management Science and Engineering, Jiangxi Normal University, Nanchang, Jiangxi, China.; Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance, Nanchang, Jiangxi, China., Dong J; School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, Jiangxi., Yang B; School of Digital Industries, Jiangxi Normal University, Nanchang, Jiangxi, China., Wang X; School of Software, Jiangxi Normal University, Nanchang, Jiangxi, China.; Research Centre for Management Science and Engineering, Jiangxi Normal University, Nanchang, Jiangxi, China.; Jiangxi Provincial Engineering Research Center of Blockchain Data Security and Governance, Nanchang, Jiangxi, China.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Dec 11; Vol. 19 (12), pp. e0313928. Date of Electronic Publication: 2024 Dec 11 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0313928
Abstrakt: The rapid expansion of online education platforms has led to an influx of false reviews, complicating users' ability to identify suitable courses promptly. Addressing these challenges, this paper introduces ICRA (Intelligent Course Review Analysis), a novel model that identifies and filters false reviews using a custom sentiment lexicon and a pre-trained ERNIE 3.0 model. ICRA enhances data quality by analyzing user reviews and course profiles comprehensively for recommendation purposes. The model utilizes the BERT lexicon and ERNIE 3.0 to obtain deep semantic representations. It integrates BiLSTM with a multi-head attention mechanism to capture essential review details, aiming to minimize overfitting and enhance generalization. By predicting user review scores and verifying review authenticity, ICRA boosts recommendation accuracy and robustness, addressing the cold-start issue. Experimental findings highlight ICRA's excellence in predicting user ratings and delivering precise course recommendations efficiently. This capability streamlines course selection on online education platforms, improving learning experiences and efficiency.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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