In this paper, we develop a reliably weighted collaborative filtering system that first tries to predict all unprovided rating data by employing context information, and then exploits both predicted and provided rating data for generating suitable recommendations. Since the predicted rating data are not a hundred percent accurate, they are weighted weaker than the provided rating data when integrating both these kinds of rating data into the recommendation process. In order to flexibly represent rating data, Dempster-Shafer (DS) theory is used for data modelling in the system. The experimental results indicate that assigning weights to rating data is capable of improving the performance of the system.
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