Recommendation and personalization research is no longer confined to computer science. Relevant questions are actively explored across disciplines including information systems, communication science, management, cognitive science, psychology, education, media studies, marketing, and the social sciences, often grounded...
Christine Bauer, Eva Zangerle, Alan Said· Proceedings of the 20th ACM...· 0 citations
Explainability in recommender systems (RS) remains a pivotal challenge. Counterfactual explanations have emerged as a particularly actionable paradigm, offering intuitive “what-if” reasoning. However, their evaluation lacks principled standards. Current metrics primarily assess whether explanations change the top-ranke...
Amir Reza Mohammadi, Andreas Peintner, Michael M. Müller et al.· ACM Transactions on Recommen...· 0 citations
Counterfactual explanations have become an important paradigm for improving the transparency of machine learning models by showing how small input changes can alter model outputs. While substantial progress has been made in generating such explanations, their evaluation remains insufficiently standardized, particularly...
Amir Reza Mohammadi, Andreas Peintner, Michael M. Müller et al.· Proceedings of the Thirty-Fi...· 6 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
