ICRA:研究了高精度的课程推模型,包括虚假评论过和ERNIE 3.0的研究
Bing Li1,2,3, Yuqi Hou1,2,3, Jiangtao Dong4
1School of Software, Jiangxi Normal University, Nanchang, Jiangxi, China.
PloS one
|December 11, 2024
概括
这项研究引入了智能课程审查分析 (ICRA) 来过在线教育平台上的假评论. 通过分析评论真实性和预测评分,ICRA改善了课程建议和用户体验.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 教育技术的教育技术
背景情况:
- 在线教育平台面临着许多虚假评论的挑战.
- 用户很难识别真正的课程评论,这会影响课程选择.
- 现有的方法很难有效地过欺诈性内容,并提供准确的建议.
研究的目的:
- 引入智能课程评论分析 (ICRA),这是一个用于识别和过虚假评论的新型模型.
- 提高课程数据的质量,提高电子学习平台上的建议准确性.
- 在课程建议中解决冷启动问题.
主要方法:
- 使用定制的情感词典和预训练的ERNIE 3.0模型进行审查分析.
- 使用BERT词典和ERNIE 3.0进行深度语义表示的评论和课程资料.
- 将BiLSTM与多头注意力机制集成在一起,以捕捉关键的审查特征并最大限度地减少过拟合.
主要成果:
- 在预测用户评论得分和验证评论真实性方面,ICRA表现出卓越的表现.
- 该模型显著提高了推的准确性和稳定性.
- 实验发现证实了有效的课程推交付.
结论:
- ICRA有效地过了虚假评论,提高了在线教育平台的数据质量.
- 该模型通过允许更准确,更有效的课程选择来增强用户体验.
- 对于冷启动问题,ICRA提供了强大的解决方案,提高了推系统的性能.
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