Towards Long-term Fairness in Recommendation

Ge, Yingqiang; Liu, Shuchang; Gao, Ruoyuan; Xian, Yikun; Li, Yunqi; Zhao, Xiangyu; Pei, Changhua; Sun, Fei; Ge, Junfeng; Ou, Wenwu; Zhang, Yongfeng

doi:10.1145/3437963.3441824

Cited by 157 publications

(94 citation statements)

References 35 publications

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“…There are also some taxonomies that classify the fairness of recommendations from other perspectives. In some studies, the fairness of recommendations is divided into individual fairness [7,32] and group fairness [3,4,14]. Group fairness is intended to eliminate the influence of specific attributes on the recommendation results for different groups so that disadvantaged groups are offered the same opportunities as the advantaged groups, whereas the goal of individual fairness is to enable similar users to be treated similarly.…”

Section: Related Workmentioning

confidence: 99%

Tfrom

Cao

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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At present, most research on the fairness of recommender systems is conducted either from the perspective of customers or from the perspective of product (or service) providers. However, such a practice ignores the fact that when fairness is guaranteed to one side, the fairness and rights of the other side are likely to reduce. In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers). From the perspective of providers, we consider the fairness of the providers' exposure in recommender system. For customers, we consider the fairness of the reduced quality of recommendation results due to the introduction of fairness measures. We theoretically analyzed the relationship between recommendation quality, customers fairness, and provider fairness, and design a two-sided fairness-aware recommendation model (TFROM) for both customers and providers. Specifically, we design two versions of TFROM for offline and online recommendation. The effectiveness of the model is verified on three real-world data sets. The experimental results show that TFROM provides better two-sided fairness while still maintaining a higher level of personalization than the baseline algorithms.

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Section: Related Workmentioning

confidence: 99%

Tfrom

Cao

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…There are efforts in the area of mitigating bias and ensuring fairness in recommender systems by using regularization terms [82,83,84], reinforcement learning [78,85] and neighborhood balancing [74].…”

Section: H Fairness In Recommender Systemsmentioning

confidence: 99%

“…Although the literature of methods is rich in methods to mitigate unfairness, not all of them are applicable to the dynamic nature of recommender systems. Ge et al [85] show that by enforcing fair decisions through static fairness criteria metrics, the system leads to unexpected unfairness in the long run and that fairness cannot be defined in a static setting without considering the long-term impact and evolution. Same as with the need to bring adaptability into the recommender system results, the need to have a dynamic view over fairness can be solved by using reinforcement learning.…”

Section: H Fairness In Recommender Systemsmentioning

confidence: 99%

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Fairness Embedded Adaptive Recommender System: A Conceptual Framework

Popa¹

2021

IJACSA

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In the current fast paced and constantly changing environment, companies should ensure that their way of interacting with user is both relevant and highly adaptive. In order to stay competitive, companies should invest in state-ofthe-art technologies that optimize the relationship with the user using increasingly available data. The most popular applications used to develop user relationship are Recommender Systems. The vast majority of the traditional recommender system considers recommendation as a static procedure and focus on a specific type of recommendation, being not very agile in adapting to new situations. Also, when implementing a Recommender System there is the need to ensure fairness in the way decisions are made upon customer data. In this paper, it is proposed a novel Reinforcement Learning-based recommender system that is highly adaptive to changes in customer behavior and focuses on ensuring both producer and consumer fairness, Fairness Embedded Adaptive Recommender System (FEARS). The approach overcomes Reinforcement Learning's main drawback in recommendation area by using a small, but meaningful action space. Also, there are presented two fairness metrics, their calculation and adaptation for usage with Reinforcement Learning, this way ensuring that the system gets to the optimal trade-off between personalization and fairness.

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“…They formulated an ILP based online optimization to ensure smooth transition of the exposure of items while guaranteeing a minimum utility for every user. Ge et al [11] explored the problem of long-term exposure fairness of items in dynamically changing groups of different popularity levels. They proposed a fairness-constrained reinforcement learning algorithm based on Constrained Markov Decision Process (CMDP), so that the model can dynamically adjust its recommendation policy.…”

Section: Introductionmentioning

confidence: 99%

User-Item Matching for Recommendation Fairness

Dong¹,

Xie

2021

IEEE Access

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As we all know, users and item-providers are two main parties of participants in recommender systems. However, most existing research efforts on recommendation were focused on better serving users and overlooked the purpose of item-providers. This paper is devoted to improve the item exposure fairness for item-providers' objective, and keep the recommendation accuracy not decreased or even improved for users' objective. We propose to set stock volume constraints on items, to be specific, limit the maximally allowable recommended times of an item to be proportional to the frequency of its being interacted in the past, which is validated to achieve superior item exposure fairness to common recommenders and thus mitigates the Matthew Effect on item popularity. With the two constraints of pre-existing recommendation length of users and our stock volumes of items, a heuristic strategy based on normalized scores and a Minimum Cost Maximum Flow (MCMF) based model are proposed to solve the optimal user-item matching problem, whose accuracy performances are even better than that of baseline algorithm in regular recommendation context, and in line with state-of-the-art enhancement of the baseline. What's more, our MCMF based strategy is parameter-free, while those counterpart algorithms have to resort to parameter traversal process to achieve their best performance.

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Towards Long-term Fairness in Recommendation

Cited by 157 publications

References 35 publications

Tfrom

Tfrom

Fairness Embedded Adaptive Recommender System: A Conceptual Framework

User-Item Matching for Recommendation Fairness

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