Eliminating Bias in Recommender Systems via Pseudo-Labeling

Addressing the non-uniform missing mechanism of rating feedback is critical to recommending items users prefer from biased real-world datasets. To tackle the challenging issue, we first define an ideal loss function that should be optimized to achieve the goal of recommendation. Then, we derive the generalization error bound of the ideal loss that alleviates the variance and the misspecification problems of the previous causal-based methods. We further propose a meta-learning method minimizing the bound. Empirical evaluation using real-world datasets validates the theoretical findings and demonstrates the practical advantages of the proposed method.