Papers › Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
Hangtong Xu, Yuanbo Xu, Chaozhuo Li, Fuzhen Zhuang
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections