  This paper leverages heterogeneous auxiliary information to address the data
sparsity problem of recommender systems. We propose a model that learns a
shared feature space from heterogeneous data, such as item descriptions,
product tags and online purchase history, to obtain better predictions. Our
model consists of autoencoders, not only for numerical and categorical data,
but also for sequential data, which enables capturing user tastes, item
characteristics and the recent dynamics of user preference. We learn the
autoencoder architecture for each data source independently in order to better
model their statistical properties. Our evaluation on two MovieLens datasets
and an e-commerce dataset shows that mean average precision and recall improve
over state-of-the-art methods.
