Fairness-Aware Recommendation of Information Curators
This work addresses fairness in personalized recommendations for users, but it is incremental as it provides only a high-level overview and preliminary results.
The paper tackles the problem of recommending information curators to users with fairness considerations, presenting preliminary experimental evidence from a real-world Twitter dataset.
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.