7 papers
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…
From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models
Jessica Quaye, Charvi Rastogi, Alicia Parrish +4
Text-to-image (T2I) models have become prevalent across numerous applications, making their robust evaluation against adversarial attacks a critical priority. Continuous access to…
Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation
Kathrin Wardatzky, Oana Inel, Luca Rossetto +1
Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas sugge…
Aligning Object Detector Bounding Boxes with Human Preference
Ombretta Strafforello, Osman S. Kayhan, Oana Inel +2
Previous work shows that humans tend to prefer large bounding boxes over small bounding boxes with the same IoU. However, we show here that commonly used object detectors predict l…
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…