Kyelem Yacouba¹, Kabore Kiswendsida Kisito¹, Ouedraogo Tounwendyam Frédéric² and Sèdes Florence³, ¹Université Joseph Ki-Zerbo, Burkina Faso, ²Université Norbert Zongo, Burkina Faso, ³IRIT, Toulouse, France

Justification of recommendations increases trust between users and the system but also generates more relevant recommendations than recommendation systems that do not incorporate it. That is why, we conducted a justification study of the recommendation for IAAS. Our comparative study shows that IAAS, which currently does not offer the opportunity to justify recommendations, needs to be improved. From the analysis of justification methods studied in this work, it appears that none of these methods can be used effectively in IAAS. That is why, we proposed a new IAAS architecture that deals separately with item classification and the extraction of the justification has added the item during recommendation generation. The item selection method remains unchanged as we plan to implement a new strategy to filter user’s reviews should now be extended to four elements: the documentary unit, the group of users, the justification and the weight. Opinion A=(UD,G,J,a). Where UD represents the documentary unit, G the user group, J is the justification and a is the weight of the recommendation.

IAAS, Justification in Recommender Systems, users reviews, weight of reviews.