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Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems

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Abstract

Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation.

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References

  1. Angwin J, Kirchner SM, Machine bias L (2016). www.propublica.org/article/machine-biasrisk-assessments-in-criminal-sentencing

  2. Bose AJ, Hamilton WL (2019) Compositional fairness constraints for graph embeddings. In: Proceedings of the 36th international conference on machine learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA, vol. 97. PMLR, pp 715–724

  3. Dai E, Wang S(2021) Say no to the discrimination: learning fair graph neural networks with limited sensitive attribute information. In: WSDM ’21, the fourteenth ACM international conference on web search and data mining, Virtual Event, Israel, March 8–12, 2021. ACM, pp 680–688. https://doi.org/10.1145/3437963.3441752

  4. Dai HT, Sheng QZ, Wei EZ, Aljubairy A, Khoa N (2021) Hetegraph: graph learning in recommender systems via graph convolutional networks. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05667-z

  5. Deldjoo Y, Anelli VW, Zamani H, Bellogin A, Di Noia T (2021) A flexible framework for evaluating user and item fairness in recommender systems. User modeling and user-adapted interaction, pp 1–55. https://doi.org/10.1007/s11257-020-09285-1

  6. Ekstrand MD, Das A, Burke R, Diaz F (2021) Fairness and discrimination in information access systems. CoRR abs/2105.05779

  7. Ekstrand MD, Tian M, Kazi MRI, Mehrpouyan H, Kluver D (2018) Exploring author gender in book rating and recommendation. In: Proceedings of the 12th ACM conference on recommender systems, RecSys 2018, Vancouver, October 2–7, 2018. ACM, pp 242–250. https://doi.org/10.1145/3240323.3240373

  8. Fan W, Ma Y, Li Q, He Y, Zhao YE, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: Liu R, White W, Mantrach A, Silvestri, F, McAuley JJ, Baeza-Yates R, Zia L (eds) The world wide web conference, WWW 2019, San Francisco, May 13–17, 2019. ACM, pp 417–426. https://doi.org/10.1145/3308558.3313488

  9. Ge Y, Liu S, Gao R, Xian Y, Li Y, Zhao X, Pei C, Sun F, Ge J, Ou W, et al (2021) Towards long-term fairness in recommendation. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 445–453

  10. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144. https://doi.org/10.1145/3422622

    Article  MathSciNet  Google Scholar 

  11. Gu W, Dong S, Zeng Z (2014) Increasing recommended effectiveness with Markov chains and purchase intervals. Neural Comput Appl 25(5):1153–1162

    Article  Google Scholar 

  12. Halvey M, Vallet D, Hannah D, Jose JM (2014) Supporting exploratory video retrieval tasks with grouping and recommendation. Inf Process Manag 50(6):876–898. https://doi.org/10.1016/j.ipm.2014.06.004

    Article  Google Scholar 

  13. Harper FM, Konstan JA (2016) The movielens datasets: history and context. ACM Trans Interact Intell Syst 5(4):191–1919. https://doi.org/10.1145/2827872

    Article  Google Scholar 

  14. He R, McAuley JJ (2016) VBPR: visual bayesian personalized ranking from implicit feedback. In: Schuurmans D, Wellman MP (eds) Proceedings of the thirtieth AAAI conference on artificial intelligence, February 12–17, 2016, Phoenix, pp. 144–150. AAAI Press. http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11914

  15. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25–30. ACM, pp 639–648 (2020). 10.1145/3397271.3401063

  16. He X, Liao L, Zhang H, Nie L, Hu X, Chua T (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, WWW 2017, Perth, Australia, April 3–7. ACM, pp 173–182 10.1145/3038912.3052569

  17. Hong B, Yu M (2019) A collaborative filtering algorithm based on correlation coefficient. Neural Comput Appl 31(12):8317–8326

    Article  Google Scholar 

  18. Huo Y, Chen B, Tang J, Zeng Y (2021) Privacy-preserving point-of-interest recommendation based on geographical and social influence. Inf Sci 543:202–218

    Article  MathSciNet  Google Scholar 

  19. Islam R, Keya KN, Zeng Z, Pan S, Foulds JR (2021) Debiasing career recommendations with neural fair collaborative filtering. In: WWW’21: the web conference 2021, Virtual Event/Ljubljana, Slovenia, April 19-23. ACM/IW3C2, , pp 3779–3790. https://doi.org/10.1145/3442381.3449904

  20. Kaya M, Bridge D, Tintarev N (2020) Ensuring fairness in group recommendations by rank-sensitive balancing of relevance. In: Fourteenth ACM conference on recommender systems, pp 101–110

  21. Kipf TN, Welling M.(2017) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24–26, 2017, conference track proceedings

  22. Koren Y, Bell RM, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. https://doi.org/10.1109/MC.2009.263

    Article  Google Scholar 

  23. Li F, Xu G, Cao L (2016) Two-level matrix factorization for recommender systems. Neural Comput Appl 27(8):2267–22788

    Article  Google Scholar 

  24. Li Y, Chen H, Fu Z, Ge Y, Zhang Y (2021) User-oriented fairness in recommendation. In: WWW ’21: the web conference 2021, Virtual Event/Ljubljana, Slovenia, April 19–23, 2021. ACM/IW3C2, pp 624–632. https://doi.org/10.1145/3442381.3449866

  25. Li Y, Chen H, Xu S, Ge Y, Zhang Y.(2021) Towards personalized fairness based on causal notion. In: SIGIR’21: the 44th international ACM SIGIR conference on research and development in information retrieval, Virtual Event, Canada, July 11–15, 2021. ACM, pp 1054–1063. https://doi.org/10.1145/3404835.3462966

  26. Liu H, Lin H, Xu B, Yang L, Lin Y, Chu Y, Fan W, Zhao N (2020) Improving social recommendations with item relationships. In: Neural information processing—27th international conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part IV, vol. 1332. Springer, pp 763–770. https://doi.org/10.1007/978-3-030-63820-7_87

  27. Liu H, Zhao N, Zhang X, Lin H, Yang L, Xu B, Lin Y, Fan W (2022) Dual constraints and adversarial learning for fair recommenders. Knowl Based Syst 239:108058. https://doi.org/10.1016/j.knosys.2021.108058

    Article  Google Scholar 

  28. Rastegarpanah B, Gummadi KP, Crovella M (2019) Fighting fire with fire: using antidote data to improve polarization and fairness of recommender systems. In: Proceedings of the twelfth ACM international conference on web search and data mining, WSDM 2019, Melbourne, VIC, Australia, February 11–15, 2019. ACM, pp 231–239. https://doi.org/10.1145/3289600.3291002

  29. Reusens M, Lemahieu W, Baesens B, Sels L (2018) Evaluating recommendation and search in the labor market. Knowl Based Syst 152:62–69. https://doi.org/10.1016/j.knosys.2018.04.007

    Article  Google Scholar 

  30. Reuters (2018) Amazon scraps secret ai recruiting tool that showed bias against women (October 12)

  31. Tejeda-Lorente Á, Porcel C, Peis E, Sanz R, Herrera-Viedma E (2014) A quality based recommender system to disseminate information in a university digital library. Inf Sci 261:52–69

    Article  Google Scholar 

  32. Tran DH, Sheng QZ, Zhang WE, Aljubairy A, Zaib M, Hamad SA, Tran NH, Khoa NLD (2021) Hetegraph: graph learning in recommender systems via graph convolutional networks. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05667-z

    Article  Google Scholar 

  33. Wan M, Ni J, Misra R, McAuley JJ (2020) Addressing marketing bias in product recommendations. In: WSDM’20: the thirteenth ACM international conference on web search and data mining, Houston, February 3–7, 2020. ACM, pp 618–626. https://doi.org/10.1145/3336191.3371855

  34. Wang X, He X, Wang M, Feng F, Chua T (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, SIGIR 2019, Paris, July 21–25, 2019. ACM, pp 165–174. https://doi.org/10.1145/3331184.3331267

  35. Wu L, Chen L, Shao P, Hong R, Wang X, Wang M (2021) Learning fair representations for recommendation: a graph-based perspective. In: WWW’21: the web conference 2021, Virtual Event/Ljubljana, Slovenia, April 19–23, 2021. ACM/IW3C2, pp 2198–2208. https://doi.org/10.1145/3442381.3450015

  36. Wu X, Yuan X, Duan C, Wu J (2019) A novel collaborative filtering algorithm of machine learning by integrating restricted Boltzmann machine and trust information. Neural Comput Appl 31(9):4685–4692

    Article  Google Scholar 

  37. Wu Y, Cao J, Xu G, Tan Y (2021) TFROM: a two-sided fairness-aware recommendation model for both customers and providers. In: SIGIR’21: the 44th international ACM SIGIR conference on research and development in information retrieval, Virtual Event, Canada, July 11–15, 2021. ACM, pp 1013–1022. https://doi.org/10.1145/3404835.3462882

  38. Yang D, Zhang D, Yu Z, Yu Z, Zeghlache D (2014) SESAME: mining user digital footprints for fine-grained preference-aware social media search. ACM Trans Internet Technol 14(4):28:1-28:24. https://doi.org/10.1145/2677209

    Article  Google Scholar 

  39. Yao S, Huang B (2017) Beyond parity: fairness objectives for collaborative filtering. In: Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4–9, 2017, Long Beach, pp 2921–2930

  40. Zhang F, Yuan NJ, Lian D, Xie X, Ma W 2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, USA, August 13–7, 2016. ACM, pp 353–362. https://doi.org/10.1145/2939672.2939673

  41. Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1):51–538

    Google Scholar 

  42. Zhou D, Liu H, Xu T, Zhang L, Zha R, Xiong H (2021) Transportation recommendation with fairness consideration. In: Database systems for advanced applications—26th international conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, proceedings, Part III, Lecture notes in computer science, vol 12683. Springer, pp 566–578

  43. Zhu Z, Wang J, Caverlee J (2020) Measuring and mitigating item under-recommendation bias in personalized ranking systems. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, SIGIR 2020, Virtual Event, China, July 25–30, 2020. ACM, pp 449–458. https://doi.org/10.1145/3397271.3401177

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Acknowledgements

This work is partially supported by Grant from the Natural Science Foundation of China (Nos. 61772103, 62076046, 62006034), the Ministry of Education Humanities and Social Science Project (No. 19YJC ZH199).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, China

    Haifeng Liu, Hongfei Lin, Bo Xu & Nan Zhao

  2. Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, China

    Yukai Wang

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  1. Haifeng Liu
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Correspondence to Hongfei Lin.

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Liu, H., Wang, Y., Lin, H. et al. Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems. Neural Comput & Applic 34, 18097–18111 (2022). https://doi.org/10.1007/s00521-022-07373-4

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