TY  - Type of reference 
TI  - Contextualizing Explanations in Recommender Systems : An Approach Based on User Interpretive Schemas 
AU  - Deo Munduku
 
AU  - Elsa Negre 
AB  - Explainable recommender systems aim to strengthen transparency and user trust by providing an explanation alongside each recommendation. However, these explanations are not interpreted uniformly : the same explanation may be understood by some users but not by others. Existing approaches based on user choice, the construction of explanatory profiles, or the highlighting of content justify a recommendation by relying on different information related to usage or content. Nevertheless, they do not take into account the way users actually interpret these explanations. To address this limitation, in this paper we propose two complementary directions : (i) identifying the internal human factors that most strongly influence the understanding of an explanation ; we hypothesize that the central factor is the interpretive schema, understood as a cognitive structure guiding the selection and understanding of information ; (ii) exploiting these factors to dynamically adapt the type, style, and level of detail of explanations. This positioning paves the way for a new generation of explainable recommender systems, capable of contextualizing explanations according to each user’s own mode of understanding, and thereby reinforcing their usefulness, readability, and the trust they inspire. 
DO  - 10.21494/ISTE.OP.2026.1451 
JF  - Open Journal in Information Systems Engineering 
KW  - Recommender systems, explanations, contextualization, personalization, interpretive schema, Systèmes de recommandation, explications, contextualisation, personnalisation, schéma d’interprétation,  
L1  - https://www.openscience.fr/IMG/pdf/iste_roisi26v6n2_5.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2026/04/22 
SN  - 2634-1468 
TT  - Contextualisation des explications dans les systèmes de recommandation : une approche basée sur les schémas d’interprétation des utilisateurs 
UR  - https://www.openscience.fr/Contextualizing-Explanations-in-Recommender-Systems-An-Approach-Based-on-User 
IS  - Special Issue INFORSID 2025 
VL  - 6 
ER  -