Multi-Cause Deconfounding for Recommender Systems with Latent Confounders

TL;DR

This paper introduces MCDCF, a novel method for recommender systems that accounts for multi-cause latent confounders affecting user feedback, improving accuracy by reducing bias through causal effect estimation.

Contribution

The paper proposes a new multi-cause deconfounding approach that learns substitutes for latent confounders in recommender systems using user behavior data.

Findings

MCDCF effectively recovers latent confounders in real-world datasets.

MCDCF reduces bias and improves recommendation accuracy.

The method is theoretically sound and empirically validated.

Abstract

In recommender systems, various latent confounding factors (e.g., user social environment and item public attractiveness) can affect user behavior, item exposure, and feedback in distinct ways. These factors may directly or indirectly impact user feedback and are often shared across items or users, making them multi-cause latent confounders. However, existing methods typically fail to account for latent confounders between users and their feedback, as well as those between items and user feedback simultaneously. To address the problem of multi-cause latent confounders, we propose a multi-cause deconfounding method for recommender systems with latent confounders (MCDCF). MCDCF leverages multi-cause causal effect estimation to learn substitutes for latent confounders associated with both users and items, using user behaviour data. Specifically, MCDCF treats the multiple items that users interact with and the multiple users that interact with items as treatment variables, enabling it to learn substitutes for the latent confounders that influence the estimation of causality between users and their feedback, as well as between items and user feedback. Additionally, we theoretically demonstrate the soundness of our MCDCF method. Extensive experiments on three real-world datasets demonstrate that our MCDCF method effectively recovers latent confounders related to users and items, reducing bias and thereby improving recommendation accuracy.