Recommendation systems only provide more specific recommendations to users. They do not consider
giving a justification for the recommendation. However, the justification for the recommendation allows the
user to make the decision whether or not to accept the recommendation. It also improves user satisfaction
and the relevance of the recommended item. However, the IAAS recommendation system that uses
advisories to make recommendations does not provide a justification for the recommendations. That is why
in this article, our task consists for helping IAAS users to justify their recommendations. For this, we
conducted a related work on architectures and approaches for justifying recommendations in order to
identify an architecture and approach suitable for the context of IAAS. From the analysis in this article, we
note that neither of these approaches uses the notices (IAAS mechanism) to justify their recommendations.
Therefore, existing architectures cannot be used in the context of IAAS. That is why, we have developed a
new IAAS architecture that deals separately with item filtration and justification extraction that
accompanied the item during recommendation generation (Figure 7). And we have improved the reviews by
adding users’ reviews on the items. The user’s notices include the Documentary Unit (DU), the user Group
(G), the Justification (J) and the weight (a); noted A=(DU,G,J,a).

IAAS,, justification of recommendations, weight of comments, relevance of recommendations, justification of recommendation architecture for IAAS.