课程推的双关系图框架
Yong Ouyang1, Zhen Ye1, Lingyu Chen1
1College of Computer Science, Hubei University of Technology, Wuhan, 430068, PR China.
概括
这项研究引入了双重关系图 (DRG) 框架,以应对教育课程推系统中的数据稀疏性. 通过模拟双重关系,DRG提高了准确性,优于单个图形方法.
科学领域:
- 教育技术
- 人工智能
- 数据科学
背景情况:
- 课程推系统对于个性化学习和提高教学质量至关重要.
- 大型语言模型 (LLM) 是有前途的,但数据稀缺性却很困难.
- 数据稀缺性限制了传统和基于LLM的推模型的准确性.
研究的目的:
- 提出一个双重关系图 (DRG) 框架,以解决课程建议中的数据稀疏性.
- 模拟课程与用户之间的关系,以提高推的准确性.
- 在稀疏的教育环境中开发可扩展和有效的个性化课程建议解决方案.
主要方法:
- 使用LLM语义推理,协作过,聚类和关联规则挖掘构建基于课程的图表.
- 通过协作过和LLM偏好推断构建基于用户的图表.
- 通过共同学习和协作推理在一个统一的管道中整合双重图.
主要成果:
- 在两个数据集中,DRG框架显著缓解了数据稀疏性,链接覆盖率增加了37.88%和12.67%.
- 与单一关系方法相比,DRG在任务排名方面表现优越.
- 拟议的DRG模块增强了传统和基于LLM的推系统.
结论:
- 双重关系图 (DRG) 框架有效地解决了教育推系统中的数据稀缺问题.
- 模拟双重关系和整合LLM驱动的语义理解可以提高建议的准确性.
- DRG是一个多功能,可插入和使用的模块,可增强现有的推模型,并提供可扩展的解决方案.
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