Debiased Model-based Interactive Recommendation
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. This is why some debiased methods have been
proposed recently. However, two essential drawbacks still remain: 1) ignoring
the dynamics of the time-varying popularity results in a false reweighting of
items. 2) taking the unknown samples as negative samples in negative sampling
results in the sampling bias. To overcome these two drawbacks, we develop a
model called \textbf{i}dentifiable \textbf{D}ebiased \textbf{M}odel-based
\textbf{I}nteractive \textbf{R}ecommendation (\textbf{iDMIR} in short). In
iDMIR, for the first drawback, we devise a debiased causal world model based on
the causal mechanism of the time-varying recommendation generation process with
identification guarantees; for the second drawback, we devise a debiased
contrastive policy, which coincides with the debiased contrastive learning and
avoids sampling bias. Moreover, we demonstrate that the proposed method not
only outperforms several latest interactive recommendation algorithms but also
enjoys diverse recommendation performance.