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Found 34 papers, 5 papers with code
De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems
Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations.
Post-Userist Recommender Systems : A Manifesto
We define userist recommendation as an approach to recommender systems framed solely in terms of the relation between the user and system.
Social Choice for Heterogeneous Fairness in Recommendation
Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests.
Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking
Synthetic data is a useful resource for algorithmic research.
Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations.
Dynamic fairness-aware recommendation through multi-agent social choice
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks.
Who Pays? Personalization, Bossiness and the Cost of Fairness
The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others.
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
This is especially problematic when bias is amplified over time as a few popular items are repeatedly over-represented in recommendation lists.
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research.
Fairness in Information Access Systems
Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems.
Fairness and Transparency in Recommendation: The Users' Perspective
Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation.
User-centered Evaluation of Popularity Bias in Recommender Systems
In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users' perspective and we propose a new metric that can address these limitations.
User Factor Adaptation for User Embedding via Multitask Learning
In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets.
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit.
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Moreover, we show that the more a group is affected by the algorithmic popularity bias, the more their recommendations are miscalibrated.
Feedback Loop and Bias Amplification in Recommender Systems
Recommendation algorithms are known to suffer from popularity bias; a few popular items are recommended frequently while the majority of other items are ignored.
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
The effectiveness of these approaches, however, has not been assessed in multistakeholder environments where in addition to the users who receive the recommendations, the utility of the suppliers of the recommended items should also be considered.
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results.
Unfair Exposure of Artists in Music Recommendation
It is well-known that the recommendation algorithms suffer from popularity bias; few popular items are over-recommended which leads to the majority of other items not getting proportionate attention.
Developing a Recommendation Benchmark for MLPerf Training and Inference
Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience.
Exploring User Opinions of Fairness in Recommender Systems
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society.
The Impact of Popularity Bias on Fairness and Calibration in Recommendation
In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users' true preferences and we consider how various algorithms may result in different degrees of miscalibration.
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
Crank up the volume: preference bias amplification in collaborative recommendation
Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences.
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
Research on fairness in machine learning has been recently extended to recommender systems.
The Unfairness of Popularity Bias in Recommendation
Recommender systems are known to suffer from the popularity bias problem: popular (i. e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations.
Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness
There is growing research interest in recommendation as a multi-stakeholder problem, one where the interests of multiple parties should be taken into account.
Flatter is better: Percentile Transformations for Recommender Systems
This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance.
Reducing Popularity Bias in Recommendation Over Time
Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure.
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes.
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all.
Synthetic Attribute Data for Evaluating Consumer-side Fairness
When evaluating recommender systems for their fairness, it may be necessary to make use of demographic attributes, which are personally sensitive and usually excluded from publicly-available data sets.
Computers and Society
Popularity-Aware Item Weighting for Long-Tail Recommendation
Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all.
