Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems

• URL: http://arxiv.org/abs/2401.04474v1
• Date: Tue, 9 Jan 2024 10:24:46 GMT
• Title: Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems
• Authors: Ngoc Luyen Le and Marie-H\'el\`ene Abel and Philippe Gouspillou
• Abstract summary: This paper presents an approach that combines embedding-based and semantic-based models to generate 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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