85 citations · 126 across the 6 of their papers we have counts for
6 papers
RecSys Fairness Metrics: Many to Use But Which One To Choose?
Jessie J. Smith, Lex Beattie
In recent years, recommendation and ranking systems have become increasingly popular on digital platforms. However, previous work has highlighted how personalized systems might lea…
Recommender Systems and Algorithmic Hate
Jessie J. Smith, Lucia Jayne, Robin Burke
Despite increasing reliance on personalization in digital platforms, many algorithms that curate content or information for users have been met with resistance. When users feel dis…
Fairness and Transparency in Recommendation: The Users' Perspective
Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus +2
Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often exp…
Exploring User Opinions of Fairness in Recommender Systems
Jessie Smith, Nasim Sonboli, Casey Fiesler +1
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents…
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Luca Belli, Sofia Ira Ktena, Alykhan Tejani +12
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is…
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems
Masoud Mansoury, Himan Abdollahpouri, Jessie Smith +3
The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and…