  Grocery recommendation is an important recommendation use-case, which aims to
predict which items a user might choose to buy in the future, based on their
shopping history. However, existing methods only represent each user and item
by single deterministic points in a low-dimensional continuous space. In
addition, most of these methods are trained by maximizing the co-occurrence
likelihood with a simple Skip-gram-based formulation, which limits the
expressive ability of their embeddings and the resulting recommendation
performance. In this paper, we propose the Variational Bayesian Context-Aware
Representation (VBCAR) model for grocery recommendation, which is a novel
variational Bayesian model that learns the user and item latent vectors by
leveraging basket context information from past user-item interactions. We
train our VBCAR model based on the Bayesian Skip-gram framework coupled with
the amortized variational inference so that it can learn more expressive latent
representations that integrate both the non-linearity and Bayesian behaviour.
Experiments conducted on a large real-world grocery recommendation dataset show
that our proposed VBCAR model can significantly outperform existing
state-of-the-art grocery recommendation methods.
