3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in exposure-fairness-1
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This work proposes a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data, and adopts a novel fairness-aware learning strategy to dynamically generate fairness signals.
This paper considers group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation, as well as demonstrating how stochastic ranking policies can be optimized towards said fairness goals.
This work develops a scalable, fast, and fair method called exposure-aware ADMM, based on implicit alternating least squares (iALS), a conventional scalable algorithm for collaborative filtering, but optimizes a regularized objective to achieve a flexible control of accuracy-fairness tradeoff.
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