Oh Hyeji, Kim Chulyun
Department of IT Engineering, Sookmyung Women's University, 100 Cheongpa-ro 47-gil, Yongsan-gu, Seoul, 04310, Korea.
Sci Rep. 2024 May 2;14(1):10125. doi: 10.1038/s41598-024-60808-x.
Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work.
公平性已成为网络中的关键价值，最新研究在诸多问题中对其进行考量。在推荐系统中，公平性很重要，因为物品的可见性由系统控制。先前的公平感知推荐系统假定用户与物品之间存在足够的关系数据。然而，新用户和新物品频繁出现且尚无关系数据的情况很常见。在本文中，我们研究在冷启动状态下增强公平性的推荐方法。当用户偏好或物品受欢迎程度未知时，公平性更为重要。我们提出一种基于元学习的冷启动推荐框架FaRM，以减轻推荐的不公平性。所提出的框架包括三个步骤。我们首先提出一种公平感知元路径生成方法，以消除敏感属性中的偏差。此外，我们通过元路径聚合方法构建公平感知用户表示。然后，我们提出一种新颖的公平目标函数，并引入联合学习方法，以最小化相关性与公平性之间的权衡。在各种冷启动场景的广泛实验中，结果表明，FaRM在公平性性能方面显著优于先前工作，同时保持相关性准确率。
