  Hybrid recommendations have recently attracted a lot of attention where user
features are utilized as auxiliary information to address the sparsity problem
caused by insufficient user-item interactions. However, extracted user features
generally contain rich multimodal information, and most of them are irrelevant
to the recommendation purpose. Therefore, excessive reliance on these features
will make the model overfit on noise and difficult to generalize. In this
article, we propose a variational bandwidth auto-encoder (VBAE) for
recommendations, aiming to address the sparsity and noise problems
simultaneously. VBAE first encodes user collaborative and feature information
into Gaussian latent variables via deep neural networks to capture non-linear
user similarities. Moreover, by considering the fusion of collaborative and
feature variables as a virtual communication channel from an
information-theoretic perspective, we introduce a user-dependent channel to
dynamically control the information allowed to be accessed from the feature
embeddings. A quantum-inspired uncertainty measurement of the hidden rating
embeddings is proposed accordingly to infer the channel bandwidth by
disentangling the uncertainty information in the ratings from the semantic
information. Through this mechanism, VBAE incorporates adequate auxiliary
information from user features if collaborative information is insufficient,
while avoiding excessive reliance on noisy user features to improve its
generalization ability to new users. Extensive experiments conducted on three
real-world datasets demonstrate the effectiveness of the proposed method. Codes
and datasets are released at https://github.com/yaochenzhu/vbae.
