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- W3115164309 abstract "How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combining multiple fair classifiers can still result in an unfair classification system. This presents a significant challenge: how do we understand and improve fairness in recommender systems composed of multiple components?" @default.
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- W3115164309 date "2021-03-08" @default.
- W3115164309 modified "2023-09-30" @default.
- W3115164309 title "Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems" @default.
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- W3115164309 doi "https://doi.org/10.1145/3437963.3441732" @default.
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