Fairness-Aware Recommendation of Information Curators Academic Article uri icon

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.

published proceedings

  • CoRR

author list (cited authors)

  • Zhu, Z., Wang, J., Zhang, Y., & Caverlee, J.

complete list of authors

  • Zhu, Ziwei||Wang, Jianling||Zhang, Yin||Caverlee, James

publication date

  • September 2018