Towards a new possibilistic collaborative filtering approach

Collaborative filtering approaches exploit users preferences to provide items recommendations. These preferences describing the actual state of the item are generally certain. However, in real problems we can not ignore the importance of uncertainty. In this paper, we propose a purely possibilistic collaborative filtering approach that provides a recommendation list given uncertain preferences expressed by possibility distributions. Experimental results show that the proposed approach outperforms traditional collaborative filtering algorithm.