  In today's data-rich environment, recommender systems play a crucial role in
decision support systems. They provide to users personalized recommendations
and explanations about these recommendations. Embedding-based models, despite
their widespread use, often suffer from a lack of interpretability, which can
undermine trust and user engagement. This paper presents an approach that
combines embedding-based and semantic-based models to generate post-hoc
explanations in recommender systems, leveraging ontology-based knowledge graphs
to improve interpretability and explainability. By organizing data within a
structured framework, ontologies enable the modeling of intricate relationships
between entities, which is essential for generating explanations. By combining
embedding-based and semantic based models for post-hoc explanations in
recommender systems, the framework we defined aims at producing meaningful and
easy-to-understand explanations, enhancing user trust and satisfaction, and
potentially promoting the adoption of recommender systems across the e-commerce
sector.
