Search Results for author:
Found 21 papers, 2 papers with code
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.
Toward the Next Generation of News Recommender Systems
This paper proposes a vision and research agenda for the next generation of news recommender systems (RS), called the table d'hote approach.
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.
Popularity Bias in Recommendation: A Multi-stakeholder Perspective
In this dissertation, I study the impact of popularity bias in recommender systems from a multi-stakeholder perspective.
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.
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
That leads to low coverage of items in recommendation lists across users (i. e. low aggregate diversity) and unfair distribution of recommended 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.
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.
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.
Incorporating System-Level Objectives into Recommender Systems
One of the most essential parts of any recommender system is personalization-- how acceptable the recommendations are from the user's perspective.
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.
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.
