Videopedia: Lecture Video Recommendation for Educational Blogs Using Topic Modeling
Videopedia: Lecture Video Recommendation for Educational Blogs Using Topic Modeling
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DOI：
10.1007/978-3-319-27671-7_20
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Subhasree Basu;Yi Yu;V. Singh;Roger Zimmermann
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文献类型：
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作者：
Subhasree Basu;Yi Yu;V. Singh;Roger Zimmermann
在线学习的教育材料的两个主要来源是电子学习博客，如Wikipedia，Edublogs等，以及托管在YouTube、Videolectures.net等各种网站上的在线视频。如果在一个集成平台上向学生展示文本和视频，学生将受益匪浅。由于这两种系统是分别设计的，因此利用这两种来源的主要挑战是如何获得与电子学习博客相关的视频材料。我们的目标是建立一个系统，无缝集成基于文本的博客和在线视频，并推荐相关的视频解释的概念，在博客。我们的算法使用从隐藏字幕生成的视频成绩单中提取的内容。我们使用主题建模来映射主题的公共语义空间中的视频和博客。在主题空间中匹配视频和博客之后，具有高相似度值的视频被推荐给博客。初步结果是合理的，并确认所提出的计划的有效性。
Two main sources of educational material for online learning are e-learning blogs like Wikipedia, Edublogs, etc., and online videos hosted on various sites like YouTube, Videolectures.net, etc. Students would benefit if both the text and videos are presented to them in an integrated platform. As the two types of systems are separately designed, the major challenge in leveraging both sources is how to obtain video materials, which are relevant to an e-learning blog. We aim to build a system that seamlessly integrates both the text-based blogs and online videos and recommends relevant videos for explaining the concepts given in a blog. Our algorithm uses content extracted from video transcripts generated by closed captions. We use topic modeling to map videos and blogs in the common semantic space of topics. After matching videos and blogs in the space of topics, videos with high similarity values are recommended for the blogs. The initial results are plausible and confirm the effectiveness of the proposed scheme.