一个基于对非正式电子学习环境的信任产生建议的框架
Amjad Rehman1, Adeel Ahmed2, Tahani Jaser Alahmadi3
1Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了一种信任意识的深度神经推 (TDNR) 框架,以解决电子学习中的学习者冷启动问题. 通过结合信任和关系数据,TDNR提高了推准确性,优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 教育技术的教育技术
背景情况:
- 在线学习环境面临信息过载和数据共享的挑战.
- 学习者冷启动问题阻碍了电子学习社区的新用户有效地回答问题.
- 现有的推系统在非正式学习环境中难以获得稀疏性和准确性.
研究的目的:
- 提出一个新的信任意识深度神经推 (TDNR) 框架,以解决学习者冷启动问题.
- 通过建模复杂的非线性关系和结合信任度来提高推准确性.
- 通过更好的专家-问者匹配,促进非正式电子学习社区的形成.
主要方法:
- 开发了一个基于信任的深度神经推 (TDNR) 框架.
- 使用隐藏的迪里克莱特分配 (LDA) 进行标签建模和使用枢纽和权威分数的专家排名.
- 在推模型中纳入了问者-回答者关系图和信任度.
- 在使用MAP,RMSE和F-measure指标的堆溢出数据集上评估模型.
主要成果:
- 与基于评级和基于社会信任的方法相比,TDNR框架显著提高了建议的准确性.
- TDNR在平均绝对误差 (MAE),根平均平方误差 (RMSE) 和F测量指标方面表现出卓越的表现.
- 该模型有效地解决了信息过载和用户稀疏性问题,在冷启动场景中提供个性化的建议.
结论:
- 拟议的TDNR框架为电子学习中的学习者冷启动问题提供了强大而可靠的解决方案.
- 通过促进支持性的学习社区和准确的信息检索,TDNR增强了用户体验.
- 混合方法有效地模拟复杂的关系,导致更相关和个性化的建议.
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