均值:在推系统中,平衡的评级注入方法以缓解受欢迎偏见
Mert Gulsoy1,2, Emre Yalcin3, Alper Bilge2
1Distance Education Research Center, Alaaddin Keykubat University, Antalya, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
EquiRate 方法通过向不太受欢迎的项目添加合成评分来打击推系统中的受欢迎偏见. 这种方法增强了建议的多样性,并优于现有的方法,特别是在大型数据集上.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 推系统经常表现出人气偏见,过度强调受欢迎的项目,忽视利基内容.
- 这种偏见源于不平衡的评级分布,其中受欢迎的项目获得不成比例的相互作用.
- 有限的多样性和内容曝光是推中受欢迎程度偏差的结果.
研究的目的:
- 提出和评估EquiRate方法,以减轻推系统中的受欢迎偏见.
- 通过平衡评级分布来增强推多样性和内容曝光.
- 引入FusionIndex指标,以全面评估推质量.
主要方法:
- EquiRate是一种预处理技术,将合成评级注入到不那么受欢迎的项目中.
- 它采用策略来选择用于合成评级注入和生成评级值的项目.
- 融合指数指标同时评估推准确性和超准确性因素.
主要成果:
- EquiRate变种有效地减少了人气偏差,并改善了基准数据集上的建议多样性.
- "融合指数"揭示了一些现有的脱皮方法在平衡准确性和其他因素方面存在困难.
- 优化的EquiRate变体在FusionIndex上显著优于现有方法,特别是在高维数据上.
结论:
- EquiRate提供了一个可行的预处理解决方案,以解决推系统中的受欢迎偏见.
- 融合指数为推质量提供了一个全面的评估框架.
- EquiRate在平衡准确性和多样性方面表现出卓越的表现,特别是在现实的大规模场景中.
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