Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.

翻译：学习用户和物品的精确表示以拟合观测到的交互数据，是协同过滤的基本任务。现有研究通常推断纠缠表示来拟合此类交互数据，忽略了用户与物品间交互背后多样化的匹配关系建模，导致性能受限且可解释性弱。针对该问题，我们提出一种用于协同推荐的双解耦变分自编码器（DualVAE），该方法将解耦表示学习与变分推断相结合，以促进隐式交互数据的生成。具体而言，我们首先通过融合注意力感知的双重解耦与解耦变分自编码器来推断用户和物品的解耦潜在表示，从而实现解耦概念。进一步地，为促进用户与物品解耦表示之间的对应性与独立性，我们设计了一种邻域增强表示约束，并辅以定制化的对比机制来提升表示质量。在三个真实基准数据集上的大量实验表明，我们提出的模型显著优于多个近期的先进基线方法。进一步的实证结果也揭示了DualVAE所学习解耦表示的可解释性。