Fair Link Prediction with Multi-Armed Bandit Algorithms
Fair Link Prediction with Multi-Armed Bandit Algorithms
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DOI：
10.1145/3578503.3583624
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Weixiang Wang;S. Soundarajan
中科院分区：
文献类型：
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作者：
Weixiang Wang;S. Soundarajan
推荐系统已被应用于许多领域，近年来，与这种系统相关的伦理问题得到了严重的关注。社交网络中的友谊或链接推荐系统中的不公平问题已经开始引起关注，因为这种不公平会导致诸如分割和回音室之类的问题。这个问题的一个挑战是，网络有许多公平性度量，现有方法只考虑单个特定公平性指标的改进[16，17，20]。在这项工作中，我们将公平链接预测问题建模为多臂强盗问题。我们提出了FairLink，一个多臂的土匪为基础的框架，预测新的边缘，都是准确的和良好的行为方面的公平性的选择。该方法允许用户指定期望的公平性度量。在5个真实数据集上的实验表明，与标准推荐算法相比，FairLink推荐算法的公平性得到了显著提高，而准确率只有很小的降低。
Recommendation systems have been used in many domains, and in recent years, ethical problems associated with such systems have gained serious attention. The problem of unfairness in friendship or link recommendation systems in social networks has begun attracting attention, as such unfairness can cause problems like segmentation and echo chambers. One challenge in this problem is that there are many fairness metrics for networks, and existing methods only consider the improvement of a single specific fairness indicator [16, 17, 20]. In this work, we model the fair link prediction problem as a multi-armed bandit problem. We propose FairLink, a multi-armed bandit based framework that predicts new edges that are both accurate and well-behaved with respect to a fairness property of choice. This method allows the user to specify the desired fairness metric. Experiments on five real-world datasets show that FairLink can achieve a significant fairness improvement as compared to a standard recommendation algorithm, with only a small reduction in accuracy.