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). In this work, we demonstrate that non-uniformity in the observed value distributions of individual entities leads to severely biased predictions in state-of-the-art models, skewing predictions towards the average of observed past values for the entity and providing worse-than-random predictive power in eccentric yet equally important cases. We show that the usage of global error metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) is insufficient to capture this phenomenon, which we name eccentricity bias, and we introduce Eccentricity-Area Under the Curve (EAUC) as a new complementary metric that can quantify it in all studied models and datasets. We also prove the adequateness of EAUC by using naive de-biasing corrections to demonstrate that a lower model bias correlates with a lower EAUC and vice-versa. This work contributes a bias-aware evaluation of dyadic regression models to avoid potential unfairness and risks in critical real-world applications of such systems.

翻译：双变量回归模型用于预测实体对之间的实值结果，在许多领域具有基础性地位（例如在推荐系统中预测用户对产品的评分），并在其他众多领域（如个性化药理学中估算患者适用的药物剂量）展现出潜力且正处于探索阶段。本研究证明，当单个实体的观测值分布呈现非均匀性时，会导致前沿模型产生严重偏差预测，使预测结果偏向该实体过往观测值的平均值，并在特殊却同等重要的案例中产生比随机预测更差的性能。我们发现，使用均方根误差（RMSE）和平均绝对误差（MAE）等全局误差指标不足以捕捉这一现象（我们称之为“特殊性偏差”），为此引入“特殊性-曲线下面积”（EAUC）作为新的补充性度量指标，该指标能在所有研究模型与数据集中量化此类偏差。通过采用简单的去偏差修正方法，我们进一步证明了EAUC的适用性：模型偏差越低则EAUC值越小，反之亦然。本研究通过提供具有偏差感知能力的双变量回归模型评估方法，为避免此类系统在关键现实应用场景中可能产生的不公平性与风险作出了贡献。