A new evidential collaborative filtering: A hybrid memory- and model-based approach

One of the most promising approaches in the field of Recommender Systems (RSs) is Collaborative Filtering (CF). CF techniques are commonly divided in the two general classes of memory-based and model-based. A wise strategy would be to combine these two methods to increase their performance while leveling out the weakness of each one. Otherwise, the uncertainty pervaded throughout the different steps of the recommendation process should not be ignored. Handling uncertainty is very challenging and important for more reliable and intelligible predictions. That is why, we propose in this paper a new CF approach which combines these two categories under the belief function theory while dealing with the uncertainty pervaded in the prediction process. The effectiveness of our proposal is validated on a real-word data set and compared to state-of-the-art CF approaches under certain and uncertain frameworks.