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- W2891845385 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." @default.
- W2891845385 created "2018-09-27" @default.
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- W2891845385 date "2018-09-09" @default.
- W2891845385 modified "2023-09-26" @default.
- W2891845385 title "Fairness-Aware Recommendation of Information Curators" @default.
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