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DataFunSummit
DataFunSummit
Oct 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, and proposes causal embedding models to mitigate unfairness while ensuring sustainable system performance.

Recommendation systemscausal inferenceconsumer perspective
0 likes · 9 min read
Fairness in Recommendation Systems: Consumer and Provider Perspectives
DataFunSummit
DataFunSummit
Jul 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, causal embedding techniques, experimental results on MovieLens and Yelp, and future research directions.

Recommendation systemsartificial intelligencecausal inference
0 likes · 9 min read
Fairness in Recommendation Systems: Consumer and Provider Perspectives