In social recommender systems, it is crucial that the recommendation models provide equitable visibility for different demographic groups, such as gender or race. Most existing research has addressed this problem by only studying individual static snapshots of networks that typically change over time. To address this gap, we study the evolution of recommendation fairness over time and its relation to dynamic network properties. We examine three real-world dynamic networks by evaluating the fairness of six recommendation algorithms and analyzing the association between fairness and network properties over time. We further study how interventions on network properties influence fairness by examining counterfactual scenarios with alternative evolution outcomes and differing network properties. Our results on empirical datasets suggest that recommendation fairness improves over time, regardless of the recommendation method. We also find that two network properties, minority ratio, and homophily ratio, exhibit stable correlations with fairness over time. Our counterfactual study further suggests that an extreme homophily ratio potentially contributes to unfair recommendations even with a balanced minority ratio. Our work provides insights into the evolution of fairness within dynamic networks in social science. We believe that our findings will help system operators and policymakers to better comprehend the implications of temporal changes and interventions targeting fairness in social networks.

翻译：在社交推荐系统中，推荐模型需为不同人口统计学群体（如性别或种族）提供平等的可见性，这一点至关重要。现有研究大多仅针对随时间变化的网络进行静态快照分析。为填补这一空白，我们研究了推荐公平性随时间演变的规律及其与动态网络属性的关系。通过评估六种推荐算法的公平性并分析其与网络属性随时间变化的关联，我们对三个真实动态网络进行了实证分析。进一步地，我们通过构建具有不同演化结果和网络属性的反事实场景，探究网络属性干预对公平性的影响。基于实证数据集的结果表明，无论采用何种推荐方法，推荐公平性均随时间推移而改善。我们还发现少数群体比例和同质性比例这两个网络属性与公平性随时间呈现稳定相关性。反事实研究表明，即使少数群体比例平衡，极端同质性比例仍可能加剧推荐不公。本研究揭示了社会科学中动态网络公平性的演变规律，相关发现将帮助系统运营者与政策制定者更深入理解时间变化及针对性干预措施对社交网络公平性的影响。