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Found 4 papers, 1 papers with code
Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models
Dyadic regression models, which predict real-valued outcomes for pairs of entities, are fundamental in many domains (e. g. predicting the rating of a user to a product in Recommender Systems) and promising and under exploration in many others (e. g. approximating the adequate dosage of a drug for a patient in personalized pharmacology).
Sustainable Transparency in Recommender Systems: Bayesian Ranking of Images for Explainability
Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens.
Sustainable Personalisation and Explainability in Dyadic Data Systems
Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others.
Explainable artificial intelligence
Image-based Recommendation Explainability
+1
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces
EADMNC leverages the formulation of the previous ADMNC model to offer pre hoc and post hoc explainability, while maintaining the accuracy of the original architecture.
