Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems
This addresses the problem of low user trust in recommender systems for e-commerce, but it appears incremental as it builds on existing methods.
The paper tackles the lack of interpretability in embedding-based recommender systems by combining them with semantic-based models using ontology-based knowledge graphs to generate post-hoc explanations, aiming to enhance user trust and satisfaction.
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.