Exposure Fairness
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Exposure Fairness
Most implemented papers
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
jasonshere/FairGAN
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Proceedings of the ACM Web Conference 2022
To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data.
Joint Multisided Exposure Fairness for Recommendation
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.
Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
theresiavr/individual-item-fairness-measures-recsys
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To our knowledge, this is the first critical comparison of individual item fairness measures in recommender systems.
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e. g., products, jobs, news, video) and their providers.
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
We find that most of these measures: i) correlate weakly with one another and even contradict each other at times; ii) are less sensitive to rank position changes than relevance- and fairness-only measures, meaning that they are less granular than traditional RS measures; and iii) tend to compress scores at the low end of their range, meaning that they are not very expressive.
