FERSF:随机模型检验引导的公平性增强推荐系统框架

FERSF:fairness-enhanced recommendation system framework guided by stochastic model checking

在推荐系统的实际应用中,物品流行度偏差会被系统的反馈循环、机器学习训练模型以及一些外界因素所放大,从而导致大量的长尾物品得不到公平的推荐机会。针对反馈循环放大流行度偏差所导致的公平性问题,首次通过随机模型检验的方法进行公平性分析和增强研究。将基于流行度偏差和反馈循环的传统推荐系统框架建模成DTMC模型,并验证其公平性。实验发现随着反馈循环轮数增加,马太效应加剧,公平性明显减弱。然后提出一种随机模型检验引导的公平性增强的推荐系统框架FERSF:在传统的推荐系统框架回路中增加一个动态公平性阈值检测过程,监测其公平性,并对反馈影响因子进行公平性增强调整以减缓流行度偏差对系统的影响。通过实验分析,与传统的推荐系统相比,FERSF的公平性显著提升;与基于效用函数的公平性改进方法相比,FERSF因结合反馈循环的动态特性,从根本上抑制流行度偏差的放大;与其他针对算法的公平性改进相比,FERSF因基于推荐系统框架建模,兼容性强。

In the practical application of recommendation systems,item popularity bias can be amplified by feedback loops,machine learning training models,and some external factors.It results in a phenomenon where a large number of long-tail items do not get a fair chance to be recommended.To address the fairness problem caused by the feedback loop amplifying the popularity bias,this paper conducted the first fairness analysis and enhancement study by means of a stochastic model checking method.It modeled the traditional recommendation system framework based on popularity bias and feedback loops as a DTMC and verified the fairness properties.The experiment revealed that as the number of feedback loop rounds increased,the Matthew effect intensified and fairness significantly diminished.This paper presented a fairness-enhanced recommendation system framework(FERSF)guided by stochastic model checking.It added a dynamic fairness threshold detection process to the feedback loop of the traditional framework to monitor the fairness.Also,it made a fairness-enhanced adjustment of the feedback influence factor to mitigate the impact of popularity bias on the system.The experimental analysis shows that the fairness of FERSF is significantly improved compared to the traditional recommendation system.Compared with the methods based on utility functions for fairness improvement,FERSF fundamentally inhibits the amplification of popularity bias due to the dynamic nature of the combined feedback loop.Compared with other algorithm-specific fairness improvements,FERSF is highly compatible because it is modeled based on the recommendation system framework.