  We propose Style Conditioned Recommendations (SCR) and introduce style
injection as a method to diversify recommendations. We use Conditional
Variational Autoencoder (CVAE) architecture, where both the encoder and decoder
are conditioned on a user profile learned from item content data. This allows
us to apply style transfer methodologies to the task of recommendations, which
we refer to as injection. To enable style injection, user profiles are learned
to be interpretable such that they express users' propensities for specific
predefined styles. These are learned via label-propagation from a dataset of
item content, with limited labeled points. To perform injection, the condition
on the encoder is learned while the condition on the decoder is selected per
explicit feedback. Explicit feedback can be taken either from a user's response
to a style or interest quiz, or from item ratings. In the absence of explicit
feedback, the condition at the encoder is applied to the decoder. We show a 12%
improvement on NDCG@20 over the traditional VAE based approach and an average
22% improvement on AUC across all classes for predicting user style profiles
against our best performing baseline. After injecting styles we compare the
user style profile to the style of the recommendations and show that injected
styles have an average +133% increase in presence. Our results show that style
injection is a powerful method to diversify recommendations while maintaining
personal relevance. Our main contribution is an application of a
semi-supervised approach that extends item labels to interpretable user
profiles.
