Evidential Item-Based Collaborative Filtering

Recommender Systems (RSs) in particular the collaborative filtering approaches have reached a high level of popularity. These approaches are designed for predicting the user’s future interests towards unrated items. However, the provided predictions should be taken with restraint because of the uncertainty pervading the real-world problems. Indeed, to not give consideration to such uncertainty may lead to unrepresentative results which can deeply affect the predictions’ accuracy as well as the user’s confidence towards the RS. In order to tackle this issue, we propose in this paper a new evidential item-based collaborative filtering approach. In our approach, we involve the belief function theory tools as well as the Evidential K-Nearest Neighbors (EKNN) classifier to deal with the uncertain aspect of items’ recommendation ignored by the classical methods. The performance of our new recommendation approach is proved through a comparative evaluation with several traditional collaborative filtering recommenders.