Year: 2014
Type: conference-paper
Abstract: One problem created by the success of video lecture repositories \nis the difficulty faced by individual users when choosing the most \nsuitable video for their learning needs from among the vast numbers \navailable on a given site. Recommender systems have become extremely \ncommon in recent years and are used in many areas. In the particular \ncase of video lectures, automatic speech transcriptions can be used to \nzoom in on user interests at a semantic level, thereby improving the \nquality of the recommendations made. In this paper, we describe a video \nlecture recommender system that uses automatic speech transcriptions, \nalongside other relevant text resources, to generate semantic lecture and \nuser models. In addition, we present a real-life implementation of this \nsystem for the VideoLectures.NET repository.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2342510576