3 papers
cs.IR2026
Rank, Don't Generate: Statement-level Ranking for Explainable Recommendation
Ben Kabongo, Arthur Satouf, Vincent Guigue
Textual explanations, generated with large language models (LLMs), are increasingly used to justify recommendations. Yet, evaluating these explanations remains a critical challenge…
cs.IR2026
On the Factual Consistency of Text-based Explainable Recommendation Models
Ben Kabongo, Vincent Guigue
Text-based explainable recommendation aims to generate natural-language explanations that justify item recommendations, to improve user trust and system transparency. Although rece…
cs.IR2025
ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations
Ben Kabongo, Vincent Guigue, Pirmin Lemberger
Collaborative filtering drives many successful recommender systems but struggles with fine-grained user-item interactions and explainability. As users increasingly seek transparent…