  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.
