保护隐私的协作推者的稳定性,反对受欢迎偏见问题
Mert Gulsoy1,2, Emre Yalcin3, Alper Bilge2
1Distance Education Research Center, Alaaddin Keykubat University, Antalya, Turkey.
PeerJ. Computer science
|August 7, 2023
概括
保护隐私的推系统为隐私意识的用户提供更公平的建议,提高多样性和新性. 然而,随着用户隐私水平的提高,普及偏差方法变得不那么有效.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 数据 隐私 数据 隐私 数据
背景情况:
- 推系统至关重要,但面临隐私问题和人气偏见.
- 用户需要在不损害个人数据的情况下提供建议.
- 人气偏见限制了推的多样性和发现.
研究的目的:
- 分析基于扰乱的随机化隐私保护协作推算法.
- 为了评估它们的有效性,反对人气偏见.
- 调查隐私级别对推质量的影响.
主要方法:
- 构建了具有不同隐私保护级别的用户角色.
- 在这些人身上仔细检查了十个推算法.
- 在实验中使用了三个现实世界数据集.
- 在保护隐私的环境中研究了普及偏差策略.
主要成果:
- 对隐私敏感的用户得到了不偏见的,更公平的建议.
- 在多样性,新性和目录覆盖率方面观察到的改善.
- 准确性经历了一种可容忍的牺牲,以获得更好的隐私.
- 人气偏差策略在更高的隐私级别下显示效率下降.
结论:
- 保护隐私的技术可以有效地减轻人气偏见.
- 平衡隐私和建议准确性是可以实现的.
- 现有的受欢迎程度偏差方法需要对隐私意识系统进行改进.
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