Fairness-Aware Recommendation of Information Curators
arXiv:1809.03040
Abstract
This paper highlights our ongoing efforts to create effective information curator recommendation models that can be personalized for individual users, while maintaining important fairness properties. Concretely, we introduce the problem of information curator recommendation, provide a high-level overview of a fairness-aware recommender, and introduce some preliminary experimental evidence over a real-world Twitter dataset. We conclude with some thoughts on future directions.
arXiv comment5 pages, 3 figures, The 2nd FATREC Workshop on Responsible Recommendation at RecSys, 2018
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- Equality of Opportunity in Supervised Learning
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