Item Recommendation with Variational Autoencoders and Heterogenous Priors
This work addresses the need for better personalized recommendations in collaborative filtering by integrating multimodal data, though it is incremental as it builds on prior VAE methods.
The paper tackles the problem of item recommendation by extending Variational Autoencoders to incorporate side information like user review text, achieving up to 29.41% relative improvement in ranking metrics over existing models.
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.