Intelligent media indexing and television recommender systems

This paper presents a state-of-art review on recommender systems that has been identified as possible areas of my research. It describes the current generation of collaborative filtering methods which are usually classified into three main categories: item-based, user-based and hybrid methods. The paper considers these methods to be applied to digital television providing recommendation for viewers. Personalised television is predicted to be the next step in the evolution of television which might reshape the whole landscape of mass media. The paper also identifies anticipated problems in the domain of recommender systems which includes indexing, collaborative filtering, ranking problems and possible research directions to solve these problems. Finally, these challenges are considered in the domain of personalised television which has its own inherent shortcomings.