A Clustering-Based Method for Automatic Educational Video Recommendation Using Deep Face-Features of Lecturers
This addresses the challenge of content discovery in educational video databases for learners, though it is an incremental improvement over existing recommendation methods.
The paper tackles the problem of recommending educational videos by using deep face-features of lecturers to cluster videos based on lecturer presence, achieving a mAP of 99.165% for ranking recommendations by lecturer screen time.
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%.