Debiasing Recommendation by Learning Identifiable Latent Confounders
This addresses bias in recommendations for users and platforms, offering a novel approach with theoretical backing, though it builds on existing causal inference methods.
The paper tackled the problem of confounding bias in recommendation systems by proposing an identifiable deconfounder method that infers latent confounders using proxy variables, achieving improved prediction accuracy with theoretical guarantees and verified effectiveness on real-world datasets.
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic status of a user) that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. However, they cannot guarantee the identification of counterfactual feedback, which can lead to biased predictions. In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. The proposed iDCF is a general deconfounded recommendation framework that applies proximal causal inference to infer the unmeasured confounders and identify the counterfactual feedback with theoretical guarantees. Extensive experiments on various real-world and synthetic datasets verify the proposed method's effectiveness and robustness.