Meta-Learning for Debiasing Recommendation using Simulated Uniform Data
Sibo Lu, Zhen Liu, Xinxin Yang, Yilin Ding, Yibo Gao, Yafan Yuan · 2024
The recommendation system is subject to various biases, resulting in different training and testing data distribution. Most previous work either relies on a part of uniform data to guide model training which is difficult to obtain, or trains without any use of uniform data. However, not using uniform data may result in the inability to observe user’s real behaviors, leading to the presence of confounding factors, which harms the performance of unbiased recommendations. In this work, we proposed a novel method IML to perform debiasing recommendations by leveraging only the statistical characteristics of uniform dataset and training data. We use Invariant Meta-Learning(IML) to learn invariant features that remain insensitive to distributional changes. Finally, we propose a sample hard-aware weighted method to enhance training. Extensive experiments on real-world datasets demonstrate the effectiveness of IML.