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.
@article{arxiv.1809.03040,
title = {Fairness-Aware Recommendation of Information Curators},
author = {Ziwei Zhu and Jianling Wang and Yin Zhang and James Caverlee},
journal= {arXiv preprint arXiv:1809.03040},
year = {2018}
}
Comments
5 pages, 3 figures, The 2nd FATREC Workshop on Responsible Recommendation at RecSys, 2018