Efficient E-Learning Recommendation by Leveraging Social Media Data

我们建议TRACE框架基于使用维基百科文本的语言学方法自动执行这样的视频分割。TRACE有两个主要贡献：1.提取了一种新的基于语言的维基百科特征来有效地分割讲座视频，2.研究了基于最新算法的视频分割结果的后期融合。这项工作已经发表在ISM 2015[3]上。我们提出了一个无缝集成基于文本的博客和在线视频的视频百科系统，并推荐相关视频来解释博客中给出的概念。我们的算法使用从隐藏字幕生成的视频转录中提取的内容。我们使用潜在狄利克雷分配(LDA)将视频和博客映射到主题的公共语义空间中。在话题空间中匹配视频和博客后，推荐相似度高的视频给博客。这项工作已经发表在MMM 2016[2]上。我们提出了一种基于主题建模的讲座视频模糊聚类方法。我们的新算法使用对自动字幕生成的视频转录进行主题建模来提取这些视频的内容。我们从维基百科中为每个聚类选择具有代表性的文本文档。结果是可信的，证实了拟议方案的有效性，并已被2016年CBMI会议接受[1]。

We suggested the TRACE framework to automatically perform such a video segmentation based on a linguistic approach using Wikipedia texts. TRACE has two main contributions: 1. the extraction of a novel linguistic-based Wikipedia feature to segment lecture videos efficiently, and 2. the investigation of a late fusion of video segmentation results derived from state-of-the-art algorithms. This work has been published in ISM 2015 [3].We proposed the Videopedia 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 Latent Dirichlet Allocation (LDA) 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. This work has been published in MMM 2016 [2].We proposed a fuzzy clustering approach for lecture videos based on topic modeling. Our novel algorithm uses topic modeling on video transcripts generated by automatic captions to extract the contents of these videos. We choose representative text documents for each of the clusters from the Wikipedia. The results are plausible and confirm the effectiveness of the proposed scheme, which have been accepted by conference CBMI 2016 [1].