Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider regarding fairness in recommender systems. Existing methods to mitigate mainstream bias do not explicitly model the importance of these non-mainstream users or, when they do, it is in a way that is not necessarily compatible with the data and recommendation model at hand. In contrast, we use the recommendation utility as a more generic and implicit proxy to quantify mainstreamness, and propose a simple user-weighting approach to incorporate it into the training process while taking the cost of potential recommendation errors into account. We provide extensive experimental results showing that quantifying mainstreamness via utility is better able at identifying non-mainstream users, and that they are indeed better served when training the model in a cost-sensitive way. This is achieved with negligible or no loss in overall recommendation accuracy, meaning that the models learn a better balance across users. In addition, we show that research of this kind, which evaluates recommendation quality at the individual user level, may not be reliable if not using enough interactions when assessing model performance.

翻译：主流偏见是推荐系统公平性的重要考量因素，部分用户因偏好小众或活跃度较低而获得低质量推荐。现有缓解主流偏见的方法要么未显式建模非主流用户的重要性，要么在建模过程中与当前数据和推荐模型存在兼容性问题。与此不同，我们采用推荐效用作为更通用且隐式的主流性度量指标，提出一种简单的用户加权方法，在考虑潜在推荐错误成本的前提下将主流性度量融入训练过程。大量实验结果表明：基于效用量化主流性更能有效识别非主流用户，且通过成本敏感方式训练模型确实能更优地服务此类用户。该方法在实现用户间更佳平衡的同时，几乎不损失整体推荐准确率。此外，研究表明，若在评估模型性能时未使用足够多的交互数据，此类基于个体用户层面的推荐质量评估可能缺乏可靠性。