research-article

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

Authors:

Guandong XuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 1

Article No.: 5, Pages 1 - 26

https://doi.org/10.1145/3586993

Published: 18 August 2023 Publication History

Abstract

When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic Fairness-Assurance STrategy for sEssion-based Recommender systems (FASTER). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.

References

[1]

Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. 2018. A reductions approach to fair classification. In Proceedings of the International Conference on Machine Learning. PMLR, 60–69.

[2]

Abolfazl Asudeh, H. V. Jagadish, Julia Stoyanovich, and Gautam Das. 2019. Designing fair ranking schemes. In Proceedings of the International Conference on Management of Data. ACM, 1259–1276.

Digital Library

[3]

Lee Averell and Andrew Heathcote. 2011. The form of the forgetting curve and the fate of memories. J. Math. Psychol. 55, 1 (2011), 25–35.

[4]

Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, et al. 2019. Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2212–2220.

Digital Library

[5]

Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum. 2018. Equity of attention: Amortizing individual fairness in rankings. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 405–414.

Digital Library

[6]

Jesus Bobadilla, Raul Lara-Cabrera, Angel Gonzalez-Prieto, and Fernando Ortega. 2021. DeepFair: Deep learning for improving fairness in recommender systems. Int. J. Interact. Multim. Artif. Intell. 6, 6 (2021), 86–95.

[7]

Ludovico Boratto, Gianni Fenu, and Mirko Marras. 2021. Connecting user and item perspectives in popularity debiasing for collaborative recommendation. Inf. Process. Manag. 58, 1 (2021), 102387.

[8]

Avishek Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In Proceedings of the International Conference on Machine Learning. PMLR, 715–724.

[9]

Robin Burke. 2017. Multisided fairness for recommendation. arXiv preprint arXiv:1707.00093 (2017).

[10]

Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. 2017. Optimized pre-processing for discrimination prevention. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 3992–4001.

[11]

Abhijnan Chakraborty, Aniko Hannak, Asia J. Biega, and Krishna Gummadi. 2017. Fair sharing for sharing economy platforms. In Fairness, Accountability and Transparency in Recommender Systems-Workshop on Responsible Recommendation.

[12]

Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Trans. Inf. Syst. 41, 3 (2023), 1–39.

Digital Library

[13]

Minjin Choi, Jinhong Kim, Joonseok Lee, Hyunjung Shim, and Jongwuk Lee. 2022. S-Walk: Accurate and scalable session-based recommendation with random walks. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 150–160.

Digital Library

[14]

Pranav Dandekar, Ashish Goel, and David T. Lee. 2013. Biased assimilation, homophily, and the dynamics of polarization. Proc. Nat. Acad. Sci. 110, 15 (2013), 5791–5796.

[15]

Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, et al. 2021. Towards long-term fairness in recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 445–453.

Digital Library

[16]

Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019. Fairness-aware ranking in search & recommendation systems with application to LinkedIn talent search. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2221–2231.

Digital Library

[17]

Shashank Gupta and Subhadeep Maji. 2020. Predicting session length for product search on e-commerce platform. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1713–1716.

Digital Library

[18]

Douglas L. Hintzman. 1976. Repetition and memory. Psychol. Learn. Motivat. 10 (1976), 47–91.

[19]

Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, Filip Radlinski, and Geri Gay. 2007. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst. 25, 2 (2007), 7.

Digital Library

[20]

Thorsten Joachims and Filip Radlinski. 2007. Search engines that learn from implicit feedback. Computer 40, 8 (2007), 34–40.

Digital Library

[21]

Chen Karako and Putra Manggala. 2018. Using image fairness representations in diversity-based re-ranking for recommendations. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. ACM, 23–28.

Digital Library

[22]

Matthew Kay, Cynthia Matuszek, and Sean A. Munson. 2015. Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 3819–3828.

Digital Library

[23]

Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang, and Aixin Sun. 2022. An attribute-driven mirror graph network for session-based recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1674–1683.

Digital Library

[24]

Hang Lei, Yin Zhao, and Longjun Cai. 2020. Multi-objective optimization for guaranteed delivery in video service platform. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3017–3025.

Digital Library

[25]

Cheng-Te Li, Cheng Hsu, and Yang Zhang. 2022. FairSR: Fairness-aware sequential recommendation through multi-task learning with preference graph embeddings. ACM Trans. Intell. Syst. Technol. 13, 1 (2022), 1–21.

Digital Library

[26]

Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In Proceedings of the ACM Conference on Information and Knowledge Management. 1419–1428.

Digital Library

[27]

Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7, 1 (2003), 76–80.

Digital Library

[28]

Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1831–1839.

Digital Library

[29]

Weiwen Liu and Robin Burke. 2018. Personalizing fairness-aware re-ranking. arXiv preprint arXiv:1809.02921 (2018).

[30]

Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020. Controlling fairness and bias in dynamic learning-to-rank. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 429–438.

Digital Library

[31]

Jaap M. J. Murre and Joeri Dros. 2015. Replication and analysis of Ebbinghaus’ forgetting curve. PloS One 10, 7 (2015), e0120644.

[32]

Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, and Elena Zheleva. 2020. Correcting for selection bias in learning-to-rank systems. In Proceedings of the Web Conference. 1863–1873.

Digital Library

[33]

Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, and Jian Pei. 2022. Heterogeneous global graph neural networks for personalized session-based recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 775–783.

Digital Library

[34]

Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. 2020. FairRec: Two-sided fairness for personalized recommendations in two-sided platforms. In Proceedings of the Web Conference. 1194–1204.

Digital Library

[35]

Ruihong Qiu, Jingjing Li, Zi Huang, and Hongzhi Yin. 2019. Rethinking the item order in session-based recommendation with graph neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 579–588.

Digital Library

[36]

Naime Ranjbar Kermany, Jian Yang, Jia Wu, and Luiz Pizzato. 2022. Fair-SRS: A fair session-based recommendation system. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 1601–1604.

[37]

Bashir Rastegarpanah, Krishna P. Gummadi, and Mark Crovella. 2019. Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 231–239.

Digital Library

[38]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452–461.

Digital Library

[39]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. 811–820.

Digital Library

[40]

Guy Shani, David Heckerman, Ronen I. Brafman, and Craig Boutilier. 2005. An MDP-based recommender system. J. Mach. Learn. Res. 6, 9 (2005).

[41]

Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, and Bamshad Mobasher. 2020. Opportunistic multi-aspect fairness through personalized re-ranking. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 239–247.

Digital Library

[42]

Edward C. Strong. 1974. The use of field experimental observations in estimating advertising recall. J. Market. Res. 11, 4 (1974), 369–378.

[43]

Theodore Vasiloudis, Hossein Vahabi, Ross Kravitz, and Valery Rashkov. 2017. Predicting session length in media streaming. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 977–980.

Digital Library

[44]

Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, and Defu Lian. 2021. A survey on session-based recommender systems. ACM Comput. Surv. 54, 7 (2021), 1–38.

Digital Library

[45]

Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. 2019. Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In Proceedings of the International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence.

[46]

Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023. A survey on the fairness of recommender systems. ACM J. ACM 41, 3 (2023), 1–43.

[47]

Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu. 2020. Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 169–178.

Digital Library

[48]

Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1791–1800.

Digital Library

[49]

Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021. Fairness-aware news recommendation with decomposed adversarial learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4462–4469.

[50]

Chen Wu and Ming Yan. 2017. Session-aware information embedding for e-commerce product recommendation. In Proceedings of the ACM on Conference on Information and Knowledge Management. 2379–2382.

Digital Library

[51]

Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 346–353.

Digital Library

[52]

Yao Wu, Jian Cao, and Guandong Xu. 2020. FAST: A fairness assured service recommendation strategy considering service capacity constraint. In Proceedings of the International Conference on Service-oriented Computing. Springer, 287–303.

Digital Library

[53]

Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, and Lizhen Cui. 2021. Self-supervised graph co-training for session-based recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2180–2190.

Digital Library

[54]

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi. 2017. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1171–1180.

Digital Library

[55]

Meike Zehlike and Carlos Castillo. 2020. Reducing disparate exposure in ranking: A learning to rank approach. In Proceedings of the Web Conference. 2849–2855.

Digital Library

[56]

Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, and Xiaofang Zhou. 2019. Feature-level deeper self-attention network for sequential recommendation. In Proceedings of the International Joint Conference on Artificial Intelligence. 4320–4326.

[57]

Qiliang Zhu, Ao Zhou, Qibo Sun, Shangguang Wang, and Fangchun Yang. 2018. FMSR: A fairness-aware mobile service recommendation method. In Proceedings of the IEEE International Conference on Web Services (ICWS). IEEE, 171–178.

[58]

Hubert A. Zielske. 1959. The remembering and forgetting of advertising. J. Market. 23, 3 (1959), 239–243.

[59]

Hubert A. Zielske and Walter A. Henry. 1980. Remembering and forgetting television ads. J. Advert. Res. 20, 2 (1980), 7–13.

Cited By

Aird AFarastu PSun JStefancová EAll CVoida AMattei NBurke R(2024)Dynamic fairness-aware recommendation through multi-agent social choiceACM Transactions on Recommender Systems10.1145/3690653Online publication date: 28-Sep-2024
https://doi.org/10.1145/3690653
Wu JNgo CChan WGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Improving Interpretable Embeddings for Ad-hoc Video Search with Generative Captions and Multi-word Concept BankProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658052(73-82)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658052
Liu SSun JDeng XWang HLiu WZhu CYang LWu C(2024)Towards platform profit-aware fairness in personalized recommendationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02149-915:10(4341-4356)Online publication date: 1-May-2024
https://doi.org/10.1007/s13042-024-02149-9
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Index Terms

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 1

January 2024

924 pages

EISSN:1558-2868

DOI:10.1145/3613513

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 18 August 2023

Online AM: 14 March 2023

Accepted: 02 March 2023

Revised: 20 February 2023

Received: 16 September 2022

Published in TOIS Volume 42, Issue 1

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Cited By

Aird AFarastu PSun JStefancová EAll CVoida AMattei NBurke R(2024)Dynamic fairness-aware recommendation through multi-agent social choiceACM Transactions on Recommender Systems10.1145/3690653Online publication date: 28-Sep-2024
https://doi.org/10.1145/3690653
Wu JNgo CChan WGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Improving Interpretable Embeddings for Ad-hoc Video Search with Generative Captions and Multi-word Concept BankProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658052(73-82)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658052
Liu SSun JDeng XWang HLiu WZhu CYang LWu C(2024)Towards platform profit-aware fairness in personalized recommendationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02149-915:10(4341-4356)Online publication date: 1-May-2024
https://doi.org/10.1007/s13042-024-02149-9
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3610302

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