TED Talk Recommender Using Speech Transcripts
Jaehoon Oh, Injung Lee, Yeon Seonwoo, Simin Sung, Ilbong Kwon, and, Jae-Gil Lee
TL;DR
This paper presents a web application that recommends TED talks based on content analysis of speech transcripts, emphasizing the importance of content in educational video recommendations.
Contribution
It introduces a novel content-based recommendation system for TED talks using speech transcripts and provides a user-friendly interface.
Findings
Effective content similarity network for TED talks
Improved relevance in video recommendations
User interface facilitates easy access to related talks
Abstract
Nowadays, online video platforms mostly recommend related videos by analyzing user-driven data such as viewing patterns, rather than the content of the videos. However, content is more important than any other element when videos aim to deliver knowledge. Therefore, we have developed a web application which recommends related TED lecture videos to the users, considering the content of the videos from the transcripts. TED Talk Recommender constructs a network for recommending videos that are similar content-wise and providing a user interface.
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