output
20022026
most citedUser-centered Evaluation of Popularity Bias in Recommender Systems

145 citations

Showing 2021 · cs.IRShow all

5 papers · 2 filters

cs.IR2021

How does the User's Knowledge of the Recommender Influence their Behavior?

Muheeb Faizan Ghori, Arman Dehpanah, Jonathan Gemmell +2

Recommender systems have become a ubiquitous part of modern web applications. They help users discover new and relevant items. Today's users, through years of interaction with thes…

cs.IR2021★ 73 cited

A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems

Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy +2

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairne…

cs.IR2021★ 2 cited

The Evaluation of Rating Systems in Team-based Battle Royale Games

Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell +1

Online competitive games have become a mainstream entertainment platform. To create a fair and exciting experience, these games use rating systems to match players with similar ski…

cs.IR2021★ 25 cited

Toward the Next Generation of News Recommender Systems

Himan Abdollahpouri, Edward Malthouse, Joseph Konstan +2

This paper proposes a vision and research agenda for the next generation of news recommender systems (RS), called the table d'hote approach. A table d'hote (translates as host's ta…

cs.IR2021★ 145 cited

User-centered Evaluation of Popularity Bias in Recommender Systems

Himan Abdollahpouri, Masoud Mansoury, Robin Burke +2

Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of oth…