Abstract
The collaborative filtering (CF) is considered as the most widely used approach in the field of Recommender Systems (RSs). It tends to predict the users’ preferences based on the users sharing similar interests. However, ignoring the uncertainty involved in the provided predictions is among the limitations related to this approach. To deal with this issue, we propose in this paper a new user-based collaborative filtering within the belief function theory. In our approach, the evidence of each similar user is taken into account and Dempster’s rule of combination is used for combining these pieces of evidence. A comparative evaluation on a real world data set shows that the proposed method outperforms traditional user-based collaborative filtering recommenders.
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Abdelkhalek, R., Boukhris, I., Elouedi, Z. (2017). A New User-Based Collaborative Filtering Under the Belief Function Theory. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_37
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