Debiased Estimation for Cross-Domain Cold Start Recommendation

Abstract

Mapping-based methods are critical solutions for Cross-Domain Cold Start Recommendation problem, which learns a mapping function to transfer knowledge from the source domain to the target domain based on overlap users between domains. However, in many CDCSR scenarios, there exists selection bias in observed overlap users due to the users being free to choose whether to rate or interact with items of a specific domain. This selection bias without proper treatment can introduce bias to the model learning process.To address the selection bias in observed overlap users, we propose two estimators for CDCSR: Inverse Propensity Weighting CDCSR Estimator (IPW-CDCSRE) and Doubly Robust CDCSR Estimator (DR-CDCSRE). IPW-CDCSRE learns propensity scores to adjust for selection bias and enhances user representations through the learned propensity model. DR-CDCSRE further employs an imputation model to consider prediction loss for non-overlap users. Additionally, we introduce the Recurrent Learning Process (RLP) to enhance the stability and effectiveness of DR-CDCSRE. To validate the effectiveness and generalization ability of the proposed estimators, we conducted extensive experiments on three real-world CDCSR scenarios, utilizing four base CDCSR models and two types of loss functions.