Fairness-aware news recommendation with decomposed adversarial learning

C Wu, F Wu, X Wang, Y Huang, X Xie - Proceedings of the AAAI …, 2021 - ojs.aaai.org
News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …
被引用次数：142 相关文章

FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

C Wu, F Wu, X Wang, Y Huang, X Xie - arXiv preprint arXiv:2006.16742, 2020 - arxiv.org
News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …
[PDF] archive.org

[PDF][PDF] FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

C Wu, F Wu, X Wang, Y Huang, X Xie - 2021 - scholar.archive.org
News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …
[PDF] aaai.org

[PDF][PDF] FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

C Wu, F Wu, X Wang, Y Huang, X Xie - 2021 - cdn.aaai.org
News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …

FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

C Wu, F Wu, X Wang, Y Huang, X Xie - hub-cache.baai.ac.cn
[1] Wang et al. Dkn: Deep knowledge-aware network for news recommendation. WWW
2018: 1835-1844.[2] Zhu et al. Dan: Deep attention neural network for news …

FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

C Wu, F Wu, X Wang, Y Huang, X Xie - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …

FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

C Wu, F Wu, X Wang, Y Huang, X Xie - hub-cache.baai.ac.cn
Background➢ A bias-aware user model• Learn bias-aware user embedding that captures
bias information related to sensitive user attribute➢ A bias-free user model• Learn bias-free …