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.

本文介绍了一种将基于嵌入和基于语义模型相结合的方法，通过利用基于本体的知识图谱来提高推荐系统中的可解释性和解释力，以生成后续解释。该框架旨在产生有意义且易于理解的解释，提高用户的信任和满意度，并潜在地促进电子商务领域中推荐系统的采用。

将基于嵌入和基于语义的模型相结合，用于推荐系统中的事后解释