Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex and entangled data. Disentangling these latent factors to uncover their underlying representation can improve the robustness, interpretability, and controllability of recommendation models. This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The CaD-VAE method considers the causal relationships between semantically related factors in real-world recommendation scenarios, rather than enforcing independence as in existing disentanglement methods. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors. The results demonstrate that CaD-VAE outperforms existing methods, offering a promising solution for disentangling complex user behavior data in recommendation systems.

翻译：推荐模型通常基于观测的用户交互数据进行训练，但用户决策过程中潜在因素之间的交互会导致数据复杂且纠缠。解耦这些潜在因素以揭示其底层表示，能够提升推荐模型的鲁棒性、可解释性和可控性。本文提出因果解耦变分自编码器（CaD-VAE），一种从推荐系统交互数据中学习因果解耦表示的新方法。CaD-VAE方法考虑了真实推荐场景中语义相关因素之间的因果关系，而非像现有解耦方法那样强制要求独立性。该方法利用结构因果模型生成描述潜在因素间因果关系的因果表示。实验结果表明，CaD-VAE性能优于现有方法，为解耦推荐系统中复杂的用户行为数据提供了一种有前景的解决方案。