Handling Uncertainty in Recommender Systems under the Belief Function Theory

Dealing with uncertainty is an important challenge in real world applications including Recommender Systems (RSs). Different kinds of uncertainty can be pervaded at any level throughout the recommendation process, which follows to inaccurate results. The main goal of this research work is to consider RSs under an uncertain framework. We seek for an improvement over the traditional recommendation approaches in order to handle such uncertainty under the belief function theory.