Ensuring User-Side Fairness In Dynamic Recommender Systems

Ensuring User side Fairness in Dynamic Recommender Systems

[rfp1296] Ensuring User-side Fairness in Dynamic Recommender Systems

Perception of Fairness in Group Music Recommender Systems

Providing Item-side Individual Fairness for Deep Recommender Systems

ACM FAccT Conference

RecSys 2020 Tutorial: Counteracting Bias and Increasing Fairness in Search and Recommender Systems

Providing Item-side Individual Fairness for Deep Recommender Systems

ACM FAccT Conference

User-oriented Fairness in Recommendation

VideoLecturesChannel

Fairness in Recommender Systems: Research Landscape and Future Directions

Andrew Le Duy Dung

#34 - Chen Xu (Renmin University of China) - Provider and User Fairness in Recommender Systems

DEFirst Reading Group - MILA x Vector

CIIR Talk Series- 4/22/2022: James Caverlee - Interaction Modeling and Fairness in Recommendation

CIIR Talk Series

Fairness and Transparency in Recommendation: The Users' Perspective

FAccTRec 2022: The 5th Workshop on Responsible Recommendation

Data privacy and fairness in recommender systems by Martin Tegner

GAIA

Fairness and Diversity in Social-Based Recommender Systems

BIAS, FAIRNESS, AND MORE IN RECOMMENDER SYSTEMS

USC Information Sciences Institute

Tutorial 2: Fairness in Rankings and Recommenders

FAT* 2018: Robin Burke - Balanced Neighborhoods for Multi-sided Fairness in Recommendation

ACM FAccT Conference

Sparse Feature Factorization for Recommender Systems with Knowledge Graphs

Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms,

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