ECFAR: A Rule-Based Collaborative Filtering System Dealing with Evidential Data

Collaborative filtering (CF) is considered as one of the most popular and widely used approaches in recommendation systems. CF makes automatic recommendations based on the similarity between users (user-based) or items (item-based) in the system. In this respect, various machine learning techniques were used to create model-based CF methods. However, most of the previous works do not consider the imperfections in the users' ratings. Thus, in this paper, we tackled the issue of creating a rule-based CF model dealing with evidential data, i.e., data where imperfection is represented and managed thanks to the belief function theory. We proposed a novel method named ECFAR that learns recommendation rules from a soft rating matrix and uses them to make predictions. To assess the reliability of our method, we conducted various experiments on a real-world data set. The experiments show that our proposed method produces satisfying results compared to existing solutions.