Abstract
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.
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Bahri, N., Bach Tobji, M.A., Ben Yaghlane, B. (2022). ECFAR: A Rule-Based Collaborative Filtering System Dealing with Evidential Data. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_88
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