Combining Embedding-Based and Semantic-Based Models for Post-Hoc Explanations in Recommender Systems - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

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

Résumé

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.

Dates et versions

hal-04454269 , version 1 (13-02-2024)

Identifiants

Citer

Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou. Combining Embedding-Based and Semantic-Based Models for Post-Hoc Explanations in Recommender Systems. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 2023, Honolulu, Hawaii, United States. pp.4619-4624, ⟨10.1109/SMC53992.2023.10394410⟩. ⟨hal-04454269⟩
15 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More