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09/05/2023
Fairness of Exposure in Dynamic Recommendation
Exposure bias is a well-known issue in recommender systems where the exp...
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09/04/2022
Exposure-Aware Recommendation using Contextual Bandits
Exposure bias is a well-known issue in recommender systems where items a...
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07/01/2022
Behavioral Player Rating in Competitive Online Shooter Games
Competitive online games use rating systems for matchmaking; progression...
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05/24/2022
Using user's local context to support local news
American local newspapers have been experiencing a large loss of reader ...
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11/29/2021
Player Modeling using Behavioral Signals in Competitive Online Games
Competitive online games use rating systems to match players with simila...
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09/02/2021
How does the User's Knowledge of the Recommender Influence their Behavior?
Recommender systems have become a ubiquitous part of modern web applicat...
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08/07/2021
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
Exposure bias is a well-known issue in recommender systems where items a...
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07/07/2021
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems tha...
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06/21/2021
Evaluating Team Skill Aggregation in Online Competitive Games
One of the main goals of online competitive games is increasing player e...
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05/28/2021
The Evaluation of Rating Systems in Team-based Battle Royale Games
Online competitive games have become a mainstream entertainment platform...
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03/11/2021
Toward the Next Generation of News Recommender Systems
This paper proposes a vision and research agenda for the next generation...
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03/10/2021
User-centered Evaluation of Popularity Bias in Recommender Systems
Recommendation and ranking systems are known to suffer from popularity b...
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08/21/2020
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Recently there has been a growing interest in fairness-aware recommender...
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08/15/2020
The Evaluation of Rating Systems in Online Free-for-All Games
Online competitive games have become increasingly popular. To ensure an ...
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07/25/2020
Feedback Loop and Bias Amplification in Recommender Systems
Recommendation algorithms are known to suffer from popularity bias; a fe...
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07/23/2020
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
Popularity bias is a well-known phenomenon in recommender systems: popul...
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06/05/2020
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation
Increasing aggregate diversity (or catalog coverage) is an important sys...
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05/21/2020
Opportunistic Multi-aspect Fairness through Personalized Re-ranking
As recommender systems have become more widespread and moved into areas ...
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05/03/2020
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
Recommender systems are often biased toward popular items. In other word...
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02/18/2020
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
The proliferation of personalized recommendation technologies has raised...
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10/13/2019
The Impact of Popularity Bias on Fairness and Calibration in Recommendation
Recently there has been a growing interest in fairness-aware recommender...
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09/13/2019
Crank up the volume: preference bias amplification in collaborative recommendation
Recommender systems are personalized: we expect the results given to a p...
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08/02/2019
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
Research on fairness in machine learning has been recently extended to r...
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07/31/2019
The Unfairness of Popularity Bias in Recommendation
Recommender systems are known to suffer from the popularity bias problem...
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07/10/2019
Flatter is better: Percentile Transformations for Recommender Systems
It is well known that explicit user ratings in recommender systems are b...
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05/14/2019
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 ...
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05/14/2019
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 ...
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01/22/2019
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
Many recommender systems suffer from popularity bias: popular items are ...
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09/29/2018
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
Recommender systems help users find relevant items of interest based on ...
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02/15/2018