Debiased Model-based Interactive Recommendation

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.

Contrastive Learning Recommendation Systems

When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting

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.

Time Series Time Series Forecasting