  Inferring user characteristics such as demographic attributes is of the
utmost importance in many user-centric applications. Demographic data is an
enabler of personalization, identity security, and other applications. Despite
that, this data is sensitive and often hard to obtain. Previous work has shown
that purchase history can be used for multi-task prediction of many demographic
fields such as gender and marital status. Here we present an embedding based
method to integrate multifaceted sequences of transaction data, together with
auxiliary relational tables, for better user modeling and demographic
prediction.
