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Found 25 papers, 7 papers with code
Correcting for Popularity Bias in Recommender Systems via Item Loss Equalization
Recommender Systems (RS) often suffer from popularity bias, where a small set of popular items dominate the recommendation results due to their high interaction rates, leaving many less popular items overlooked.
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
In this paper, we study exposure bias in a class of well-known contextual bandit algorithms known as Linear Cascading Bandits.
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations generated by the system.
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Debiasing methods aim to mitigate the effect of selection bias on the evaluation and optimization of RSs.
Potential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered Analysis
Users with different degree of tolerance toward popular items are not fairly served by the recommendation system: users interested in less popular items receive more popular items in their recommendations, while users interested in popular items are recommended what they want.
Predictive Uncertainty-based Bias Mitigation in Ranking
Prior work on bias mitigation often assumes that ranking scores, which correspond to the utility that a document holds for a user, can be accurately determined.
Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
This study explores the potential of reinforcement learning algorithms to enhance career planning processes.
Fairness of Exposure in Dynamic Recommendation
However, less work has been done on addressing exposure bias in a dynamic recommendation setting where the system is operating over time, the recommendation model and the input data are dynamically updated with ongoing user feedback on recommended items at each round.
Exposure-Aware Recommendation using Contextual Bandits
This is especially problematic when bias is amplified over time as a few items (e. g., popular ones) are repeatedly over-represented in recommendation lists and users' interactions with those items will amplify bias towards those items over time resulting in a feedback loop.
Understanding and Mitigating Multi-Sided Exposure Bias in Recommender Systems
The experiments on different publicly-available datasets and comparison with various baselines confirm the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.
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.
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.
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.
Multi-sided Exposure Bias in Recommendation
Using several recommendation algorithms and two publicly available datasets in music and movie domains, we empirically show the inherent popularity bias of the algorithms and how this bias impacts different stakeholders such as users and suppliers of the items.
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.
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations.
The Relationship between the Consistency of Users' Ratings and Recommendation Calibration
In this paper, we aim to explore the relationship between the consistency of users' ratings behavior and the degree of calibrated recommendations they receive.
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.
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.
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.
