Causal Structure Representation Learning of Confounders in Latent Space
  for Recommendation
Hangtong Xu Yuanbo Xu Yongjian Yang
Inferring user preferences from the historical feedback of users is a
valuable problem in recommender systems. Conventional approaches often rely on
the assumption that user preferences in the feedback data are equivalent to the
real user preferences without additional noise, which simplifies the problem
modeling. However, there are various confounders during user-item interactions,
such as weather and even the recommendation system itself. Therefore,
neglecting the influence of confounders will result in inaccurate user
preferences and suboptimal performance of the model. Furthermore, the
unobservability of confounders poses a challenge in further addressing the
problem. To address these issues, we refine the problem and propose a more
rational solution. Specifically, we consider the influence of confounders,
disentangle them from user preferences in the latent space, and employ causal
graphs to model their interdependencies without specific labels. By cleverly
combining local and global causal graphs, we capture the user-specificity of
confounders on user preferences. We theoretically demonstrate the
identifiability of the obtained causal graph. Finally, we propose our model
based on Variational Autoencoders, named Causal Structure representation
learning of Confounders in latent space (CSC). We conducted extensive
experiments on one synthetic dataset and five real-world datasets,
demonstrating the superiority of our model. Furthermore, we demonstrate that
the learned causal representations of confounders are controllable, potentially
offering users fine-grained control over the objectives of their recommendation
lists with the learned causal graphs.