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Found 32 papers, 4 papers with code
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.
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.
Behavioral Player Rating in Competitive Online Shooter Games
Competitive online games use rating systems for matchmaking; progression-based algorithms that estimate the skill level of players with interpretable ratings in terms of the outcome of the games they played.
Using user's local context to support local news
With this subscription model, there is a need to increase user engagement and personalization, and recommender systems are one way for these news companies to accomplish this goal.
Player Modeling using Behavioral Signals in Competitive Online Games
We then use the created models to predict ranks for different groups of players in the data.
How does the User's Knowledge of the Recommender Influence their Behavior?
Recommender systems have become a ubiquitous part of modern web applications.
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.
Evaluating Team Skill Aggregation in Online Competitive Games
Rating systems leverage statistical estimation to rate players' skills and use skill ratings to predict rank before matching players.
The Evaluation of Rating Systems in Team-based Battle Royale Games
It alleviated most of the challenges faced by the other metrics while adding the freedom to adjust the focus of the evaluations on different groups of players.
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.
The Evaluation of Rating Systems in Online Free-for-All Games
However, less attention has been given to the evaluation metrics of these systems.
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.
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
In particular, our results show that the proposed solution is quite effective in increasing aggregate diversity and item-side utility while optimizing recommendation accuracy for end users.
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.
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 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.
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.
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.
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system.
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models
In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preferences.
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.
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
In the second approach the HMM is used directly to generate recommendations taking into account the identified change points.
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.
