基于频率和时间领域的混合注意力机制的MOOC课程推系统的研究
Hongli Yuan1, Li Liu1, Yiwen Zhang1
1Big Data and Artificial Intelligence College, Anhui Xinhua University, Hefei, China.
PloS one
|December 30, 2025
概括
本研究介绍了课程推系统的混合注意网络,通过分析频率和时间领域的用户行为来增强个性化学习. 这种新的方法提高了推准确度,优于现有方法.
科学领域:
- 教育技术的教育技术
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 课程推系统对于在线教育平台至关重要,可以个性化学习体验.
- 现有的序列模型 (例如,BERT4Rec,LightSANs) 专注于时间用户行为,忽视频率域分析.
- 这种局限性阻碍了准确的用户行为模式特征,特别是从稀疏数据的长期利益.
研究的目的:
- 为大规模开放在线课程 (MOOCs) 建议提出一个新的混合注意力网络.
- 为增强用户行为分析共同建模频域和时间域特征.
- 解决现有模型在捕捉稳定的长期用户利益方面的局限性.
主要方法:
- 开发了一个混合注意网络,集成频率域和时间域特征提取.
- 使用快速里埃转换 (FFT) 来从用户行为序列中提取频率域特征.
- 利用自我注意力机制捕捉时间动态,使协作双域特征建模.
主要成果:
- 拟议的模型在MooCCube数据集上取得了卓越的性能,Hit Ratio@10,MRR@10和NDCG@10分别为0.4534,0.2018,0.2618.
- 在课程推任务中表现优于当前主流推算法.
- 废弃性研究显示了显著的性能改善 (约. 10%在NDCG@10,5%在Hit@10) 采用双域融合与单域方法相比.
结论:
- 混合注意网络有效地模拟双域特征,克服课程推中的性能瓶.
- 共同分析频率和时间域,可以更全面地描述用户行为.
- 这项研究为改善在线教育中个性化课程建议提供了一条新的技术途径.
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