When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups. In various instances, certain provider groups find themselves underrepresented in the item catalog, a situation that can influence recommendation results. Hence, platform owners often seek to regulate the exposure of these provider groups in the recommended lists. In this paper, we propose a novel cost-sensitive approach designed to guarantee these target exposure levels in pairwise recommendation models. This approach quantifies, and consequently mitigate, the discrepancies between the volume of recommendations allocated to groups and their contribution in the item catalog, under the principle of equity. Our results show that this approach, while aligning groups exposure with their assigned levels, does not compromise to the original recommendation utility. Source code and pre-processed data can be retrieved at https://github.com/alessandraperniciano/meta-learning-strategy-fair-provider-exposure.

翻译：在设计推荐服务时，需兼顾所有内容供应商（包括新进者及少数族裔群体）的利益。在某些情况下，特定供应商群体会在项目目录中代表性不足，这一状况可能影响推荐结果。因此，平台所有者常需规约这些供应商群体在推荐列表中的曝光量。本文提出了一种新型代价敏感方法，旨在确保成对推荐模型中目标曝光水平的实现。该方法基于公平原则，量化并进而缓解了分配给各群体的推荐量与其在项目目录中贡献度之间的偏差。实验结果表明，该方法在使群体曝光度与预设水平对齐的同时，并未损害原始推荐效用。源代码及预处理数据可于 https://github.com/alessandraperniciano/meta-learning-strategy-fair-provider-exposure 获取。