An Evidential Collaborative Filtering Dealing with Sparsity Problem and Data Imperfections

  • Raoua AbdelkhalekEmail author
  • Imen BoukhrisEmail author
  • Zied ElouediEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


One of the most promising approaches commonly used in Recommender Systems (RSs) is Collaborative Filtering (CF). It relies on a matrix of user-item ratings and makes use of past users’ ratings to generate predictions. Nonetheless, a large amount of ratings in the typical user-item matrix may be unavailable. The insufficiency of available rating data is referred to as the sparsity problem, one of the major issues that limit the quality of recommendations and the applicability of CF. Generally, the final predictions are represented as a certain rating score. This does not reflect the reality which is related to uncertainty and imprecision by nature. Dealing with data imperfections is another fundamental challenge in RSs allowing more reliable and intelligible predictions. Thereupon, we propose in this paper a Collaborative Filtering system that not only tackles the sparsity problem but also deals with data imperfections using the belief function theory.


Recommender Systems Collaborative Filtering User-based Item-based Sparsity Belief function theory Uncertainty 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LARODEC, Institut Supérieur de Gestion de TunisUniversité de TunisTunisTunisia

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