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7115321 
Journal Article 
Beyond Parity: Fairness Objectives for Collaborative Filtering 
Yao, S; Huang, B; , 
2017 
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) 
LA JOLLA 
WOS:000452649402095 
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness. 
Guyon, I; Luxburg, UV; Bengio, S; Wallach, H; Fergus, R; Vishwanathan, S; Garnett, R; 
31st Annual Conference on Neural Information Processing Systems (NIPS) 
Long Beach, CA