Author

Robin D. Burke

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#artificial intelligence Preprint Oct 2026

Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study

This reproducibility paper explores the counterfactual explanation technique known as Learning to eXplain Recommendations (LXR), and considers alternate baselines for LXR, including item popularity and latent factor similarity, and finds that for some tasks these alternatives work surprisingly well.

Meysam Varasteh, Elizabeth McKinnie, Robin D. Burke · 0 citations

Preprint Aug 2026

Why didn't more people see it? Recommendation: Transparency for providers

Transparency in recommender systems has been widely studied from the perspective of those receiving recommendations, yet the needs of item providers, the creators whose content is distributed through these platforms, remain largely unexplored. Providers often lack insight into how their items do or do not receive expos...

Meysam Varasteh, Robin D. Burke · 0 citations