用模拟数据研究公平性重排序，避免隐私风险。
Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking
- 通过潜变量模拟生成推荐系统输出数据
- 支持对公平性重排序算法的全面评估
- 适合研究公平推荐与隐私保护的学者
合成数据是算法研究的有力资源，可在现实场景难以实现的条件下评估系统性能。在推荐系统中，现有工作多集中于大规模用户-物品交互数据的构建，但公平性推荐研究仍受限。本文提出一种新型合成数据：模拟的推荐系统输出，用于研究公平性重排序算法。该方法无需依赖需保护隐私的真实敏感数据，即可分析受保护群体及其交互行为，为公平性推荐提供可控、可重复的研究环境。
原文摘要 · Abstract (English)
Synthetic data is a useful resource for algorithmic research. It allows for the evaluation of systems under a range of conditions that might be difficult to achieve in real world settings. In recommender systems, the use of synthetic data is somewhat limited; some work has concentrated on building user-item interaction data at large scale. We believe that fairness-aware recommendation research can benefit from simulated data as it allows the study of protected groups and their interactions without depending on sensitive data that needs privacy protection. In this paper, we propose a novel type of data for fairness-aware recommendation: synthetic recommender system outputs that can be used to study re-ranking algorithms.
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