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JUCS - Journal of Universal Computer Science 28(10): 1003-1029
https://doi.org/10.3897/jucs.79777 (28 Oct 2022)
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JUCS - Journal of Universal Computer Science 28(10): 1003-1029
doi: 10.3897/jucs.79777
Received: 25 Dec 2021 | Approved: 28 Jul 2022 | Published: 28 Oct 2022
This article is part of:
JUCS - Journal of Universal Computer Science 28(10)
Authors
   
Raoua Abdelkhalek - Corresponding author
LARODEC, Institut Supérieur de Gestion, Université de Tunis, Tunis, Tunisia, Tunis, Tunisia
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Imen Boukhris
LARODEC, Institut Supérieur de Gestion, Université de Tunis, Tunis, Tunisia, Tunis, Tunisia
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Zied Elouedi
LARODEC, Institut Supérieur de Gestion, Université de Tunis, Tunis, Tunisia, Tunis, Tunisia
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Author contributions

The main contributions of this work are as follows: 

 - Proposing a new hybrid framework for the neighbors' ratings modeling and the prediction process within the belief function theory.  
- Building a trustworthy recommender that improves access and proactively provides relevant items to users by considering their preferences. It improves users' confidence and enhances the chance to succeed for such systems.  
- Helping the users making a better decision by allowing them to get a closer picture about their potentially future ratings. 
- Experiments on real world data sets to evaluate the effectiveness of our proposal.  


Conflict of interest
The authors have declared that no competing interests exist.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
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