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.

翻译：在当今数据丰富的环境中，推荐系统在决策支持系统中发挥着关键作用。它们向用户提供个性化推荐及相应的解释说明。尽管嵌入模型应用广泛，但其常因缺乏可解释性而削弱用户信任与参与度。本文提出一种方法，将基于嵌入的模型与基于语义的模型相结合，在推荐系统中生成事后解释，并利用基于本体的知识图谱提升可解释性与可说明性。通过将数据组织在结构化框架内，本体能够对实体间的复杂关系进行建模，这对于生成解释至关重要。通过结合嵌入模型与语义模型在推荐系统中生成事后解释，我们定义的框架旨在产生有意义且易于理解的解释，增强用户信任与满意度，并可能推动推荐系统在电子商务领域的广泛应用。