no code implementations • 24 Feb 2024 • Zijian Li, Ruichu Cai, Haiqin Huang, Sili Zhang, Yuguang Yan, Zhifeng Hao, Zhenghua Dong
Existing model-based interactive recommendation systems are trained by querying a world model to capture the user preference, but learning the world model from historical logged data will easily suffer from bias issues such as popularity bias and sampling bias.
1 code implementation • 15 Mar 2023 • Chen Xu, Jun Xu, Xu Chen, Zhenghua Dong, Ji-Rong Wen
According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data.
1 code implementation • 12 Mar 2023 • Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, Zhenghua Dong
In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem.