论文标题
一种基于聚类的方法，用于使用讲师的深层表面的自动教育视频建议
A Clustering-Based Method for Automatic Educational Video Recommendation Using Deep Face-Features of Lecturers
论文作者
论文摘要
在教育视频基础中发现和访问特定内容是一项艰巨的任务，这主要是因为视频内容丰富及其多样性。推荐系统通常用于增强查找和选择内容的能力。但是，推荐机制，尤其是基于文本信息的机制，会表现出一些局限性，例如容易发生的关键字或由于语音识别不精确。本文介绍了一种使用讲师的深层表面作用而无需识别的方法。更确切地说，我们使用一种无监督的面部聚类机制来基于讲师的存在在视频之间建立关系。然后，对于选定的教育视频作为参考，我们建议检测到同一讲师的存在。此外，我们根据参考讲师的出现时间对这些推荐的视频进行排名。对于此任务，我们达到了99.165％的地图值。
Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity. Recommender systems are often used to enhance the ability to find and select content. But, recommendation mechanisms, especially those based on textual information, exhibit some limitations, such as being error-prone to manually created keywords or due to imprecise speech recognition. This paper presents a method for generating educational video recommendation using deep face-features of lecturers without identifying them. More precisely, we use an unsupervised face clustering mechanism to create relations among the videos based on the lecturer's presence. Then, for a selected educational video taken as a reference, we recommend the ones where the presence of the same lecturers is detected. Moreover, we rank these recommended videos based on the amount of time the referenced lecturers were present. For this task, we achieved a mAP value of 99.165%.
