Title: Item Recommendation with Variational Autoencoders and Heterogenous Priors,
Abstract: In recent years, Variational Autoencoders (VAEs) have been shown to be highly
effective in both standard collaborative filtering applications and extensions
such as incorporation of implicit feedback. We extend VAEs to collaborative
filtering with side information, for instance when ratings are combined with
explicit text feedback from the user. Instead of using a user-agnostic standard
Gaussian prior, we incorporate user-dependent priors in the latent VAE space to
encode users' preferences as functions of the review text. Taking into account
both the rating and the text information to represent users in this multimodal
latent space is promising to improve recommendation quality. Our proposed model
is shown to outperform the existing VAE models for collaborative filtering (up
to 29.41% relative improvement in ranking metric) along with other baselines
that incorporate both user ratings and text for item recommendation.