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Mitigating Dual Latent Confounding Biases in Recommender Systems

Jianfeng Deng, Qingfeng Chen, Debo Cheng, Jiuyong Li, Lin Liu, Xiaojing Du

TL;DR

A novel debiasing method that jointly integrates the Instrumental Variables (IV) approach and identifiable Variational Auto-Encoder (iVAE) for Debiased representation learning in Recommendation systems, referred to as IViDR, which outperforms state-of-the-art models in reducing bias and providing reliable recommendations.

Abstract

Recommender systems are extensively utilised across various areas to predict user preferences for personalised experiences and enhanced user engagement and satisfaction. Traditional recommender systems, however, are complicated by confounding bias, particularly in the presence of latent confounders that affect both item exposure and user feedback. Existing debiasing methods often fail to capture the complex interactions caused by latent confounders in interaction data, especially when dual latent confounders affect both the user and item sides. To address this, we propose a novel debiasing method that jointly integrates the Instrumental Variables (IV) approach and identifiable Variational Auto-Encoder (iVAE) for Debiased representation learning in Recommendation systems, referred to as IViDR. Specifically, IViDR leverages the embeddings of user features as IVs to address confounding bias caused by latent confounders between items and user feedback, and reconstructs the embedding of items to obtain debiased interaction data. Moreover, IViDR employs an Identifiable Variational Auto-Encoder (iVAE) to infer identifiable representations of latent confounders between item exposure and user feedback from both the original and debiased interaction data. Additionally, we provide theoretical analyses of the soundness of using IV and the identifiability of the latent representations. Extensive experiments on both synthetic and real-world datasets demonstrate that IViDR outperforms state-of-the-art models in reducing bias and providing reliable recommendations.

Mitigating Dual Latent Confounding Biases in Recommender Systems

TL;DR

A novel debiasing method that jointly integrates the Instrumental Variables (IV) approach and identifiable Variational Auto-Encoder (iVAE) for Debiased representation learning in Recommendation systems, referred to as IViDR, which outperforms state-of-the-art models in reducing bias and providing reliable recommendations.

Abstract

Recommender systems are extensively utilised across various areas to predict user preferences for personalised experiences and enhanced user engagement and satisfaction. Traditional recommender systems, however, are complicated by confounding bias, particularly in the presence of latent confounders that affect both item exposure and user feedback. Existing debiasing methods often fail to capture the complex interactions caused by latent confounders in interaction data, especially when dual latent confounders affect both the user and item sides. To address this, we propose a novel debiasing method that jointly integrates the Instrumental Variables (IV) approach and identifiable Variational Auto-Encoder (iVAE) for Debiased representation learning in Recommendation systems, referred to as IViDR. Specifically, IViDR leverages the embeddings of user features as IVs to address confounding bias caused by latent confounders between items and user feedback, and reconstructs the embedding of items to obtain debiased interaction data. Moreover, IViDR employs an Identifiable Variational Auto-Encoder (iVAE) to infer identifiable representations of latent confounders between item exposure and user feedback from both the original and debiased interaction data. Additionally, we provide theoretical analyses of the soundness of using IV and the identifiability of the latent representations. Extensive experiments on both synthetic and real-world datasets demonstrate that IViDR outperforms state-of-the-art models in reducing bias and providing reliable recommendations.

Paper Structure

This paper contains 39 sections, 3 theorems, 32 equations, 13 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional Recommendation Algorithms
  4. Causal Recommendation Algorithms for Addressing Specific Biases
  5. Preliminaries
  6. Notations
  7. Instrumental Variable (IV) Approach
  8. The Proposed IViDR Method
  9. An Overview for IViDR Method
  10. The Soundness of IV in IViDR Method
  11. Interaction Data Reconstruction
  12. Construction of treatments and IVs.
  13. Treatment reconstruction.
  14. Interaction data reconstruction.
  15. Learning the Latent Representation Using iVAE in IViDR Method
  16. ...and 24 more sections

Key Result

Theorem 1

Given a causal DAG $\mathcal{G} = (\mathbf{Z} \cup \mathbf{W} \cup \mathbf{C} \cup \mathbf{B} \cup \{\mathbf{T}, \mathbf{A}, \mathbf{R}\}, \mathbf{E})$, where $\mathbf{T}$ and $\mathbf{R}$ are the treatment and outcome, respectively, and $\mathbf{E}$ is the set of edges between the variables. Let $\

Figures (13)

  • Figure 1: An example DAG illustrating that $\mathbf{Z}$ serves as an IV. $\mathbf{T}$ and $\mathbf{R}$ represent the treatment and outcome variables, respectively, while $\mathbf{B}$ denotes latent confounders without proxy variables. $\mathbf{C}$ denotes latent confounders with proxy variables, $\mathbf{A}$ indicates exposure status, and $\mathbf{W}$ represents the set of proxy variables.
  • Figure 2: The architecture of our proposed IViDR method. IViDR uses the embeddings of user features as IVs to decompose treatments into fitted (the values fitted by the regression model) and residual (the residuals) components. It then reconstructs the treatments and combines them with interactional data to generate debiased interactional data. An iVAE is employed to infer latent representations from proxy variables, interactional data, and debiased interactional data. Finally, IViDR adjusts for these latent representations to mitigate confounding biases.
  • Figure 4: The performance of all methods on the three real-world datasets.
  • Figure 5: IViDR-T, IViDR-F, IViDR-R, and IViDR algorithms' recommendation performance on the Coat, Yahoo!R3, and KuaiRand datasets.
  • Figure : (a) Ground truth.
  • ...and 8 more figures

Theorems & Definitions (6)

  • Definition 1: Instrumental Variable (IV) 20
  • Theorem 1
  • Definition 2: Identifiability classes
  • Definition 3
  • Theorem 2
  • Theorem 3