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群体（如性别或种族）提供公平可见性至关重要。现有研究多基于网络快照的静态分析，忽略了网络通常随时间演变的特性。为填补这一空白，本文研究了推荐公平性随时间的演化规律及其与动态网络属性的关系。我们通过评估六种推荐算法的公平性，并分析公平性与网络属性随时间变化的关联，对三个真实动态网络进行了实证研究。进一步，我们构建了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。同时发现少数群体比例与同质性比例两个网络属性与公平性呈现稳定的时序相关性。反事实研究进一步揭示，即使少数群体比例均衡，极端同质性比例仍可能导致不公推荐。本研究为社会科学中动态网络的公平性演化提供了深刻见解，并有望协助系统运营商与政策制定者更清晰地理解时间变化及针对公平性干预措施的社会网络影响。