  Variational Autoencoders (VAEs) have recently shown promising performance in
collaborative filtering with implicit feedback. These existing recommendation
models learn user representations to reconstruct or predict user preferences.
We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in
which VAEs jointly learn both user and item representations and collectively
reconstruct and predict user preferences. This design allows JoVA to capture
user-user and item-item correlations simultaneously. By extending the objective
function of JoVA with a hinge-based pairwise loss function (JoVA-Hinge), we
further specialize it for top-k recommendation with implicit feedback. Our
extensive experiments on several real-world datasets show that JoVA-Hinge
outperforms a broad set of state-of-the-art collaborative filtering methods,
under a variety of commonly-used metrics. Our empirical results also confirm
the outperformance of JoVA-Hinge over existing methods for cold-start users
with a limited number of training data.
