Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems
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.
Cite
@article{arxiv.2401.04474,
title = {Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems},
author = {Ngoc Luyen Le and Marie-Hélène Abel and Philippe Gouspillou},
journal= {arXiv preprint arXiv:2401.04474},
year = {2024}
}