Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations

Karako, Chen; Manggala, Putra

doi:10.1145/3213586.3226206

“…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. Approaches can also be classified from the perspective of the time that the mechanism works in the system [38], and the fairness mechanism is divided into pre-processing [10], in-processing [2,8,37] and post-processing [21,25] approaches. Our study considers both individual and group fairness.…”

Section: Related Workmentioning

confidence: 99%

Tfrom

Wu

¹

,

Cao

²

,

Xu

³

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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“…Recently, new notions of fairness (e.g. multi-sided fairness [31]) involving more than one type of stakeholder and protected group were proposed for recommender systems: recommendations could be fair not only for the clients but also for the reviewers or providers of a service [102], or also for items presented in the system [84,90,170,210].…”

Section: Overviewmentioning

confidence: 99%

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

Balayn

¹

,

Lofi

²

,

Houben

³

2021

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The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.

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“…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. Approaches can also be classified from the perspective of the time that the mechanism works in the system [38], and the fairness mechanism is divided into pre-processing [10], in-processing [2,8,37] and post-processing [21,25] approaches. Our study considers both individual and group fairness.…”

Section: Related Workmentioning

confidence: 99%

TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers

Wu,

Cao,

Xu

et al. 2021

Preprint

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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.

show abstract

Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations

Cited by 52 publications

References 19 publications

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Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers

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