Fairness-aware Bandit-based Recommendation. Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback. However, unfairness could incur in personalized recommendation.
In this paper, we study how to achieve user-side fairness in bandit based recommendation. We formulate our fair personalized recommendation as a modified.
Our algorithm detects and monitors unfairness during personalized online recommendation. We provide a theoretical regret analysis and show that our algorithm ...
Oct 7, 2020 · Abstract:Considerable research effort has been guided towards algorithmic fairness but there is still no major breakthrough.
Missing: Recommendation. | Show results with:Recommendation.
We formulate our fair personalized recommendation as a modified contextual bandit and focus on achieving fairness on the individual whom is being recommended ...
Sep 14, 2022 · We formulate our fair personalized recommendation as a modified contextual bandit and focus on achieving fairness on the individual whom is ...
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Considerable research effort has been guided towards algorithmic fairness but there is still no major breakthrough. In practice, an exhaustive search over ...
It is shown that Fairband can efficiently navigate the fairness-accuracy trade-off through hyperparameter optimization, and consistently finds ...
Missing: Recommendation. | Show results with:Recommendation.
We define a fairness-aware regret, which we call r-Regret, that takes into account the above fair- ness constraints and extends the conventional notion of ...
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