Recommender systems are widely used to provide personalized recommendations to users. Recent research has shown that recommender systems may be subject to different types of biases, such as popularity bias, leading to an uneven distribution of recommendation exposure among producer groups. To mitigate this, producer-centered fairness re-ranking (PFR) approaches have been proposed to ensure equitable recommendation utility across groups. However, these approaches overlook the harm they may cause to within-group individuals associated with colder items, which are items with few or no interactions. This study reproduces previous PFR approaches and shows that they significantly harm colder items, leading to a fairness gap for these items in both advantaged and disadvantaged groups. Surprisingly, the unfair base recommendation models were providing greater exposure opportunities to these individual cold items, even though at the group level, they appeared to be unfair. To address this issue, the study proposes an amendment to the PFR approach that regulates the number of colder items recommended by the system. This modification achieves a balance between accuracy and producer fairness while optimizing the selection of colder items within each group, thereby preventing or reducing harm to within-group individuals and augmenting the novelty of all recommended items. The proposed method is able to register an increase in sub-group fairness (SGF) from 0.3104 to 0.3782, 0.6156, and 0.9442 while also improving group-level fairness (GF) (112% and 37% with respect to base models and traditional PFR). Moreover, the proposed method achieves these improvements with minimal or no reduction in accuracy (or even an increase sometimes). We evaluate the proposed method on various recommendation datasets and demonstrate promising results independent of the underlying model or datasets.

翻译：推荐系统被广泛用于为用户提供个性化推荐。近年来的研究表明，推荐系统可能受到多种偏差的影响，例如流行度偏差，导致推荐曝光在生产者组间分布不均。为缓解这一问题，研究者提出了以生产者为中心的公平性重排序（PFR）方法，以确保各组间的推荐效用公平。然而，这些方法忽略了它们可能对与冷门物品（即交互数据极少或为零的物品）相关的组内个体造成的损害。本研究复现了先前的PFR方法，并表明这些方法显著损害了冷门物品，导致这些物品在优势组和劣势组中均存在公平性差距。令人惊讶的是，原本不公的基准推荐模型在组层面看似不公，却为这些冷门个体物品提供了更多的曝光机会。为解决此问题，本研究提出对PFR方法进行改进，通过调节系统推荐的冷门物品数量。这一修改在精确度和生产者公平性之间取得了平衡，同时优化了每个组内冷门物品的选择，从而防止或减少了对组内个体的损害，并提升了所有推荐物品的新颖性。所提出的方法能够将子组公平性（SGF）从0.3104提升至0.3782、0.6156和0.9442，同时改善组级公平性（GF）（相较于基准模型和传统PFR分别提升112%和37%）。此外，该方法在实现这些改进的同时，仅以极小的精确度下降为代价（有时甚至能提升精确度）。我们在多种推荐数据集上评估了所提出的方法，并展示了其与底层模型或数据集无关的显著效果。