A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.

中文翻译：

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神经公平协同过滤

越来越多的人际互动在社交媒体平台上被数字化并受制于算法决策，确保这些算法的公平对待变得越来越重要。在这项工作中，我们调查了在社交媒体数据上训练的协作过滤推荐系统中的性别偏见。我们开发了神经公平协同过滤 (NFCF)，这是一个实用的框架，用于使用神经协同过滤的预训练和微调方法来减轻推荐敏感项目（例如工作、学术集中或学习课程）时的性别偏见，并增强偏差校正技术。我们分别在 MovieLens 数据集和 Facebook 数据集上展示了我们的方法在无性别偏见的职业和大学专业推荐方面的效用，