MOOCDR-VSI:一种融合视频字幕信息的MOOC资源动态推荐模型  

MOOCDR-VSI:A MOOC Resource Dynamic Recommendation Model Fusing Video Subtitle Information

作　　者：吴水秀[1] 罗贤增 钟茂生[1,2] 吴如萍 罗玮[1] Wu Shuixiu;Luo Xianzeng;Zhong Maosheng;Wu Ruping;Luo Wei(School of Computer Information Engineering,Jiangxi Normal University,Nanchang 330022;Jiangxi Education Digitizing Transformation Research Center(Jiangxi Normal University),Nanchang 330022)

机构地区：[1]江西师范大学计算机信息工程学院,南昌330022 [2]江西师范大学江西教育数字化转型研究中心,南昌330022

出　　处：《计算机研究与发展》2024年第2期470-480,共11页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(61877031);江西省教育厅科技计划项目(GJJ210324)。

摘　　要：学习者在面对浩如烟海的在线学习课程资源时往往存在“信息过载”和“信息迷航”等问题,基于学习者的学习记录,向学习者推荐与其知识偏好和学习需求相符的MOOC资源变得愈加重要.针对现有MOOC推荐方法没有充分利用MOOC视频中所蕴含的隐式信息,容易形成“蚕茧效应”以及难以捕获学习者动态变化的学习需求和兴趣等问题,提出了一种融合视频字幕信息的动态MOOC推荐模型MOOCDR-VSI,模型以BERT为编码器,通过融入多头注意力机制深度挖掘MOOC视频字幕文本的语义信息,采用基于LSTM架构的网络动态捕捉学习者随着学习不断变化的知识偏好状态,引入注意力机制挖掘MOOC视频之间的个性信息和共性信息,最后结合学习者的知识偏好状态推荐出召回概率Top N的MOOC视频.实验在真实学习场景下收集的数据集MOOCCube分析了MOOCDR-VSI的性能,结果表明,提出的模型在HR@5,HR@10,NDCG@5,NDCG@10,NDCG@20评价指标上比目前最优方法分别提高了2.35%,2.79%,0.69%,2.2%,3.32%.Learners often have problems such as“information overload”and“information trek”when facing the vast online learning course resources.Based on learners’learning records,it is increasingly important to recommend MOOC courses to learners that are consistent with their knowledge preferences and learning needs.Aiming at the problems that the existing MOOC recommendation methods do not make full use of the implicit information contained in MOOC videos,which are easy to form a“cocoon effect”,and it is difficult to capture the dynamic learning needs and interests of learners,a dynamic MOOC recommendation model integrating video subtitle information MOOCDR-VSI is proposed,which uses BERT as the encoder to deeply mine the semantic information of MOOC video subtitle text by integrating the multi-head attention mechanism.The network based on LSTM architecture is used to dynamically capture the changing knowledge preference state of learners with learning,introduce the attention mechanism to mine the personality information and common information between MOOC videos,and finally recommend MOOC videos with Top N recall probability combined with the knowledge preference status of learners.The performance of MOOCDR-VSI is analyzed by MOOCCube in the experimental dataset collected in the real learning scenario,and the results show that the proposed model improves the HR@5,HR@10,NDCG@5,NDCG@10,NDCG@20 evaluation indexes by 2.35%,2.79%,0.69%,2.2%and 3.32%,respectively,compared with the current most optimal method.

关 键 词：MOOC推荐 BERT 多头注意力机制 字幕信息 长短期记忆 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]