Go to AAAI 2025 Workshop AICT homepageCalibrating Multiple Robust Learning for Causal Recommendation
Keywords: causal recommendation, multiple robust, calibration
TL;DR: We extend the previous DR calibration to MR calibration by calibrating the linear combination of multiple imputation and propensity models using bi-level optimization.
Abstract: Recommendation systems (RS) has become integral to numerous applications, ranging from e-commerce to content streaming.
A critical problem in RS is that the ratings are missing not at random (MNAR), which is due to the user can self-select the item to rate, resulting in inaccurate rating prediction for all user-item pairs. Doubly robust (DR) learning has
been studied in many tasks in RS, which is unbiased when either a single imputation or a single propensity model is accurate. In addition, multiple robust (MR) has been proposed with multiple imputation models and propensity models, and is unbiased when there exists a linear combination of these imputation models and propensity models is correct. However, we claim that the imputed errors and propensity scores are miscalibrated in the MR method. In this paper, we propose a calibrated multiple robust learning method to enhance the debiasing performance and reliability of the rating prediction model. Specifically, we propose to use bi-level optimization to solve the weights and model coefficients of each propensity and imputation model in MR framework. Moreover, we adopt the differentiable expected calibration error as part of the objective to optimize the model calibration quality directly. Experiments on three real-world datasets show that our method outperforms the state-of-the-art baselines.
Submission Number: 11
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