Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1777-1783

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

Wang Chuqin
Liu Yang
College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210046, China
DOI: 10.19734/j.issn.1001-3695.2022.09.0485

Abstract

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.

Foundation Support

国家自然科学基金资助项目(61303022)
江苏省“六大人才高峰”高层次人才资助项目(RJFW-014)
江苏省高等学校自然科学研究重大项目(17KJA520002)
南京留学人员科技创新项目择优资助项目
江苏省研究生科研创新计划资助项目(WCQXW21001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.09.0485
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Software Technology Research
Pages: 1777-1783
Serial Number: 1001-3695(2023)06-028-1777-07

Publish History

[2022-12-12] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

王楚钦, 刘阳. FERSF:随机模型检验引导的公平性增强推荐系统框架 [J]. 计算机应用研究, 2023, 40 (6): 1777-1783. (Wang Chuqin, Liu Yang. FERSF: fairness-enhanced recommendation system framework guided by stochastic model checking [J]. Application Research of Computers, 2023, 40 (6): 1777-1783. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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