May 13, 2024 · Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of ...
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May 22, 2024 · Empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy.
Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler,.
Finally, empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy. KEYWORDS meta- ...
Oct 3, 2024 · This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of ...
Mar 14, 2024 · [rfp0327] Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling. 24 views · 9 months ago ...more. ACM SIGWEB. 1.06K.
“Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling” is a paper by Zheng Zhang Qi Liu Zirui Hu Yi Zhan Zhenya Huang Weibo Gao Qingyang Mao ...
Code for the WWW'2024 paper "Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling". Run .\run.sh. BibTex. If you find this work useful in ...
May 13, 2024 · Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling · Author Picture Zheng Zhang,; Author Picture Qi Liu,; Author Picture ...
2024. Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling. Z Zhang, Q Liu, Z Hu, Y Zhan, Z Huang, W Gao, Q Mao. Proceedings of the ACM on ...