Abstract
Collaborative Filtering (CF) is one of the most successful approaches in Recommender Systems (RS). It exploits the ratings of similar users or similar items in order to predict the users’ preferences. To do so, clustering CF approaches have been proposed to group items or users into different clusters. However, most of the existing approaches do not consider the impact of uncertainty involved during the clusters assignments. To tackle this issue, we propose in this paper a clustering approach for CF under the belief function theory. In our approach, we involve the Evidential C-Means to group the most similar items into different clusters and the predictions are then performed. Our approach tends to take into account the different memberships of the items clusters while maintaining a good scalability and recommendation performance. A comparative evaluation on a real world data set shows that the proposed method outperforms the previous evidential collaborative filtering.
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Abdelkhalek, R., Boukhris, I., Elouedi, Z. (2017). A Clustering Approach for Collaborative Filtering Under the Belief Function Framework. In: Antonucci, A., Cholvy, L., Papini, O. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017. Lecture Notes in Computer Science(), vol 10369. Springer, Cham. https://doi.org/10.1007/978-3-319-61581-3_16
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