4 citations · 6 across the 3 of their papers we have counts for
4 papers
The Utility of LLMs in Recommender Systems Explanation Evaluation
Kathrin Wardatzky, Oana Inel, Luca Rossetto +1
Explanations play a crucial role in creating trustworthy recommender systems (RS), yet choosing a good explanation method presents challenges. Many explanation methods exist, but l…
D-RDW: Diversity-Driven Random Walks for News Recommender Systems
Runze Li, Lucien Heitz, Oana Inel +1
This paper introduces Diversity-Driven RandomWalks (D-RDW), a lightweight algorithm and re-ranking technique that generates diverse news recommendations. D-RDW is a societal recomm…
Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
Lucien Heitz, Runze Li, Oana Inel +1
Norm-aware recommender systems have gained increased attention, especially for diversity optimization. The recommender systems community has well-established experimentation pipeli…
DWARF: Disease-weighted network for attention map refinement
Haozhe Luo, Aurélie Pahud de Mortanges, Oana Inel +2
The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations. This study a…