A Clustering Approach for Collaborative Filtering Under the Belief Function Framework

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)

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.

Keywords

Collaborative filtering Belief function theory Clustering Evidential C-Means 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Raoua Abdelkhalek
    • 1
  • Imen Boukhris
    • 1
  • Zied Elouedi
    • 1
  1. 1.LARODEC, Institut Supérieur de Gestion de TunisUniversité de TunisTunisTunisia

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