Recommendation Generation Justified for Information Access Assistance Service (IAAS): Study of Architectural Approaches and Post-Hoc Algorithm

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. 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).