The impact of data characteristics on the performance of classical recommender systems has been recently investigated and produced fruitful results about the relationship they have with recommendation accuracy. This work provides a systematic study on the impact of broadly chosen data characteristics (DCs) of recommender systems. This is applied to the accuracy and fairness of several variations of CF recommendation models. We focus on a suite of DCs that capture properties about the structure of the user–item interaction matrix, the rating frequency, item properties, or the distribution of rating values. Experimental validation of the proposed system involved large-scale experiments by performing 23,400 recommendation simulations on three real-world datasets in the movie (ML-100K and ML-1M) and book domains (BookCrossing). The validation results show that the investigated DCs in some cases can have up to 90% of explanatory power – on several variations of classical CF algorithms –, while they can explain – in the best case – about 40% of fairness results (measured according to user gender and age sensitive attributes). Therefore, this work evidences that it is more difficult to explain variations in performance when dealing with fairness dimension than accuracy.

最近研究了数据特征对经典推荐系统性能的影响，并在它们与推荐准确性的关系方面产生了富有成效的结果。这项工作对推荐系统的广泛选择的数据特征 (DC) 的影响进行了系统研究。这适用于 CF 推荐模型的几种变体的准确性和公平性。我们专注于一组 DC，它们捕获有关用户-项目交互矩阵的结构、评级频率、项目属性或评级值分布的属性。通过对电影中的三个真实世界数据集（ML-100K和ML-1M）执行 23,400 次推荐模拟，所提出系统的实验验证涉及大规模实验) 和书籍域 ( BookCrossing )。验证结果表明，在某些情况下，调查的 DCs 可以具有高达 90% 的解释力——对经典 CF 算法的几种变体——而它们可以解释——在最好的情况下——大约 40% 的公平结果（根据用户性别和年龄敏感属性）。因此，这项工作证明，在处理公平性维度时，解释性能变化比处理准确性更困难。