Abstract
One problem created by the success of video lecture repositories is the difficulty faced by individual users when choosing the most suitable video for their learning needs from among the vast numbers
available on a given site. Recommender systems have become extremely common in recent years and are used in many areas. In the particular case of video lectures, automatic speech transcriptions can be used to zoom in on user interests at a semantic level, thereby improving the
quality of the recommendations made. In this paper, we describe a video lecture recommender system that uses automatic speech transcriptions, alongside other relevant text resources, to generate semantic lecture and user models. In addition, we present a real-life implementation of this system for the VideoLectures.NET repository.
