Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline

no code implementations • 28 Apr 2020 • Luca Belli, Sofia Ira Ktena, Alykhan Tejani, Alexandre Lung-Yut-Fon, Frank Portman, Xiao Zhu, Yuanpu Xie, Akshay Gupta, Michael Bronstein, Amra Delić, Gabriele Sottocornola, Walter Anelli, Nazareno Andrade, Jessie Smith, Wenzhe Shi

Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives.

Recommendation Systems

Exploring User Opinions of Fairness in Recommender Systems

no code implementations • 13 Mar 2020 • Jessie Smith, Nasim Sonboli, Casey Fiesler, Robin Burke

Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society.

Fairness Recommendation Systems

Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems

no code implementations • 18 Feb 2020 • Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher

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.

Fairness Recommendation Systems