  Category recommendation for users on an e-Commerce platform is an important
task as it dictates the flow of traffic through the website. It is therefore
important to surface precise and diverse category recommendations to aid the
users' journey through the platform and to help them discover new groups of
items. An often understated part in category recommendation is users'
proclivity to repeat purchases. The structure of this temporal behavior can be
harvested for better category recommendations and in this work, we attempt to
harness this through variational inference. Further, to enhance the variational
inference based optimization, we initialize the optimizer at better starting
points through the well known Metapath2Vec algorithm. We demonstrate our
results on two real-world datasets and show that our model outperforms standard
baseline methods.
