Fairness in Recommendation: A Survey

Li, Yunqi; Chen, Hanxiong; Xu, Shaofa; Ge, Yingqiang; Tan, Juntao; Liu, Shuchang; Zhang, Yongfeng

doi:10.48550/arxiv.2205.13619

Cited by 13 publications

(24 citation statements)

References 115 publications

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“…Fair recommendation models focus on providing fair recommendation results based on certain fairness definitions, which can be roughly divided into three categories: pre-processing methods, in-processing methods and post-processing methods [178,180]. Figure 4 illustrates the differences between them.…”

Section: Methods For Fairness In Recommendationmentioning

confidence: 99%

“…Recommender Systems have been considered as "benevolent" systems for a long time, which assist users (e.g., by helping them find relevant items) and create value for businesses (e.g., higher sales or increased customer retention) [141]. However, in the most recent years, considerable concerns from both academia and industry have been raised regarding the issue of fairness in recommendation [178]. Several studies argue that RS may be vulnerable to unfairness in several aspects, which may result in detrimental consequences for underrepresented or disadvantaged groups [109,176,180,254].…”

Section: Fairnessmentioning

confidence: 99%

“…Bias and discrimination are two common concepts related to the unfairness issue in machine learning and unfairness stems from both of these factors [178,202]. In recommender systems, there are two main types of biases: bias in data, and bias in algorithm [178]. The biases in data may come from the processes of data generation, data collection, or data storage.…”

Section: The Source Of Unfairness In Recommendationmentioning

confidence: 99%

“…It is worth noting that bias and discrimination are not the only reasons of unfairness. For example, researches have shown that some fairness definitions cannot be satisfied simultaneously [68,152,226], as a result, the violation of one fairness definition may be caused by ensuring another fairness definition [178].…”

Section: The Source Of Unfairness In Recommendationmentioning

confidence: 99%

“…Differences from Existing Surveys in Recommendation. There have already been several recent surveys focusing on specific ethical topics in recommendation scenario, such as explainability [61,340], bias and fairness [54,77,178,225,288,328], privacy protection [140,308], user controllability [142,143], etc. These surveys successfully highlight the importance of the social responsibility of recommender systems, leading to further developments in this important line of research.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Survey on Trustworthy Recommender Systems

Ge¹,

Liu²,

Fu³

et al. 2022

Preprint

Self Cite

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Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired counter-effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy and responsible recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy and responsible recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.

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Section: Methods For Fairness In Recommendationmentioning

confidence: 99%

Section: Fairnessmentioning

confidence: 99%

Section: The Source Of Unfairness In Recommendationmentioning

confidence: 99%

Section: The Source Of Unfairness In Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Trustworthy Recommender Systems

Ge¹,

Liu²,

Fu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Fairness in recommender systems: research landscape and future directions

Deldjoo

Jannach

Bellogín

et al. 2023

User Model User-Adap Inter

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Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.

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Measuring Item Global Residual Value for Fair Recommendation

Wang

Jiang

et al. 2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

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In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various recommendation systems are designed not only to satisfy users' personalized information needs but also to allocate items' exposure. However, recent recommendation studies mainly focus on modeling user preferences to present satisfying results and maximize user interactions, while paying little attention to developing item-side fair exposure mechanisms for rational information delivery. This may lead to serious resource allocation problems on the item side, such as the Snowball Effect. Furthermore, unfair exposure mechanisms may hurt recommendation performance. In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items. We first conduct empirical analyses of feed scenarios to explore exposure problems between items with distinct uploaded times. This points out that unfair exposure caused by the time factor may be the major cause of the Snowball Effect. Then, we propose to explicitly model item-level customized timeliness distribution, Global Residual Value (GRV), for fair resource allocation. This GRV module is introduced into recommendations with the designed Timeliness-aware Fair Recommendation Framework (TaFR). Extensive experiments on two datasets demonstrate that TaFR achieves consistent improvements with various backbone recommendation models. By modeling item-side customized Global Residual Value, we achieve a fairer distribution of resources and, at the same time, improve recommendation performance. CCS CONCEPTS• Information systems → Recommender systems.

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Fairness in Recommendation: A Survey

Cited by 13 publications

References 115 publications

A Survey on Trustworthy Recommender Systems

A Survey on Trustworthy Recommender Systems

Fairness in recommender systems: research landscape and future directions

Measuring Item Global Residual Value for Fair Recommendation

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