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.
no code implementations • 20 Feb 2024 • Zijian Li, Ruichu Cai, Zhenhui Yang, Haiqin Huang, Guangyi Chen, Yifan Shen, Zhengming Chen, Xiangchen Song, Zhifeng Hao, Kun Zhang
To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur.