User behavior data, such as ratings and clicks, has been widely used to build personalizing models for recommender systems. However, many unflattering factors (e.g., popularity, ranking position, users’ selection) significantly affect the performance of the learned recommendation model. Most existing work on unbiased recommendation addressed these biases from sample granularity (e.g., sample reweighting, data augmentation) or from the perspective of representation learning (e.g., bias-modeling). However, these methods are usually designed for a specific bias, lacking the universal capability to handle complex situations where multiple biases co-exist. Besides, rare work frees itself from laborious and sophisticated debiasing configurations (e.g., propensity scores, imputed values, or user behavior-generating process).

Towards this research gap, in this paper, we propose a universal Generative framework for Bias Disentanglement termed as GBD, constantly generating calibration perturbations for the intermediate representations during training to keep them from being affected by the bias. Specifically, a bias-identifier that tries to retrieve the bias-related information from the representations is first introduced. Subsequently, the calibration perturbations are generated to significantly deteriorate the bias-identifier’s performance, making the bias gradually disentangled from the calibrated representations. Therefore, without relying on notorious debiasing configurations, a bias-agnostic model is obtained under the guidance of the bias identifier. We further present its universality by subsuming the representative biases and their mixture under the proposed framework. Finally, extensive experiments on the real-world, synthetic, and semi-synthetic datasets have demonstrated the superiority of the proposed approach against a wide range of recommendation debiasing methods. The code is available at https://github.com/Zhidan-Wang/GBD.

用户行为数据（例如评分和点击）已被广泛用于构建推荐系统的个性化模型。然而，许多不利因素（例如，受欢迎程度、排名位置、用户选择）显着影响学习推荐模型的性能。大多数关于无偏推荐的现有工作从样本粒度（例如，样本重新加权、数据增强）或从表示学习（例如，偏差建模）的角度解决了这些偏差。然而，这些方法通常是针对特定偏差而设计的，缺乏处理多种偏差共存的复杂情况的通用能力。此外，很少有工作能够摆脱费力且复杂的去偏配置（例如倾向得分、估算值或用户行为生成过程）。

针对这一研究空白，在本文中，我们提出了一种通用的偏差消除生成框架，称为GBD ，在训练过程中不断为中间表示生成校准扰动，以防止它们受到偏差的影响。具体来说，首先引入了一个试图从表示中检索与偏见相关的信息的偏见标识符。随后，生成校准扰动以显着恶化偏差识别器的性能，使得偏差逐渐从校准表示中脱离。因此，在不依赖臭名昭著的去偏置配置的情况下，在偏置标识符的指导下获得偏置不可知的模型。我们通过将代表性偏差及其混合纳入所提出的框架来进一步展示其普遍性。最后，对现实世界、合成和半合成数据集的广泛实验证明了所提出的方法相对于各种推荐去偏方法的优越性。代码可在 https://github.com/Zhidan-Wang/GBD 获取。