  Robust recommendation aims at capturing true preference of users from noisy
data, for which there are two lines of methods have been proposed. One is based
on noise injection, and the other is to adopt the generative model Variational
Auto-encoder (VAE). However, the existing works still face two challenges.
First, the noise injection based methods often draw the noise from a fixed
noise distribution given in advance, while in real world, the noise
distributions of different users and items may differ from each other due to
personal behaviors and item usage patterns. Second, the VAE based models are
not expressive enough to capture the true preference since VAE often yields an
embedding space of a single modal, while in real world, user-item interactions
usually exhibit multi-modality on user preference distribution. In this paper,
we propose a novel model called Dual Adversarial Variational Embedding (DAVE)
for robust recommendation, which can provide personalized noise reduction for
different users and items, and capture the multi-modality of the embedding
space, by combining the advantages of VAE and adversarial training between the
introduced auxiliary discriminators and the variational inference networks. The
extensive experiments conducted on real datasets verify the effectiveness of
DAVE on robust recommendation.
