Content-based movie recommendation within learning contexts

A good movie is like a good book. As a good book can serve entertaining and learning purposes, so does a movie. In addition to that, movies are in general more engaging and reach a wider audience. In this work, we present and evaluate a method that overcomes the challenge of generating recommendations among heterogeneous resources. In our case, we recommend movies in the context of a learning object. We evaluate our method with 60 participants that judged the relevance of the recommendations. Results show that, in over 74% of the cases the recommendations are in fact related to the given learning object, outperforming a text-based recommendation approach. The implications of our work can take learning outside the classroom and invoke it during the joy of watching a movie.