Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation

Hangtong Xu, Yuanbo Xu, Chaozhuo Li, Fuzhen Zhuang

TL;DR

The paper tackles confounding in recommender systems by jointly learning latent user preferences and hidden confounders through a causal-structure framework. It introduces CSA-VAE, a variational autoencoder that integrates global and local causal graphs to disentangle confounders in latent space and to model user-specific confounder effects. The approach provides identifiability guarantees and supports counterfactual do-operations for user-controllable recommendations, with extensive experiments on synthetic and real-world datasets showing improved recall and ranking metrics. This work offers a principled, causal, and controllable mechanism to mitigate unobserved confounding in recommendations, potentially improving personalization and user satisfaction in practical settings.

Abstract

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.

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation

TL;DR

The paper tackles confounding in recommender systems by jointly learning latent user preferences and hidden confounders through a causal-structure framework. It introduces CSA-VAE, a variational autoencoder that integrates global and local causal graphs to disentangle confounders in latent space and to model user-specific confounder effects. The approach provides identifiability guarantees and supports counterfactual do-operations for user-controllable recommendations, with extensive experiments on synthetic and real-world datasets showing improved recall and ranking metrics. This work offers a principled, causal, and controllable mechanism to mitigate unobserved confounding in recommendations, potentially improving personalization and user satisfaction in practical settings.

Abstract

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.
Paper Structure (47 sections, 1 theorem, 31 equations, 13 figures, 4 tables)

This paper contains 47 sections, 1 theorem, 31 equations, 13 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Deconfound in Recommendation
  4. Causal Structure Learning
  5. Methodology
  6. A More Rational Architecture
  7. Causal Modeling of Unobserved Confounders
  8. Causal Structure Learning
  9. Global SCM
  10. Local SCM
  11. Causal Layer
  12. Mask Layer
  13. Identification of the Learned Graph
  14. Mix Layer
  15. Inference and Learning Strategy
  16. ...and 32 more sections

Key Result

Proposition 1

Assume a restricted ANM with graph $\mathcal{G}$ and distribution $P(\textbf{C})$ so that the original SEM is identifiable. If the parameterized SEM in the form of Eq. eq:8 with graph $\mathcal{H}$ induces the same $P(\textbf{C})$, then $\mathcal{H}$ is a super-graph of $\mathcal{G}$.

Figures (13)

  • Figure 1: An example illustrating that user preferences in the feedback data are influenced by both the users themselves and external confounders(e.g., temperature, location).
  • Figure 2: Conventional modeling versus a more rational modeling approach. Left is the conventional modeling, and right is the rational modeling proposed in this work. U$\rightarrow$User; I$\rightarrow$Item; C$\rightarrow$Confounders; P$\rightarrow$Preference in feedback data; Y$\rightarrow$User feedback. The exogenous variables of nodes (e.g., C) are not displayed in the graph.
  • Figure 3: An example showing confounders disentanglement in latent space, where the confounders C are further dismantled to k concepts $\{c_1,c_2,...,c_k\}$ based on the given causal graph.
  • Figure 4: Example of a Global and Local Causal Structure.
  • Figure 5: The architecture of CSA-VAE.
  • ...and 8 more figures

Theorems & Definitions (1)

  • Proposition 1