  User profiling has long been an important problem that investigates user
interests in many real applications. Some recent works regard users and their
interacted objects as entities of a graph and turn the problem into a node
classification task. However, they neglect the difference of distinct
interaction types, e.g. user clicks an item v.s.user purchases an item, and
thus cannot incorporate such information well. To solve these issues, we
propose to leverage the relation-aware heterogeneous graph method for user
profiling, which also allows capturing significant meta relations. We adopt the
query, key, and value mechanism in a transformer fashion for heterogeneous
message passing so that entities can effectively interact with each other. Via
such interactions on different relation types, our model can generate
representations with rich information for the user profile prediction. We
conduct experiments on two real-world e-commerce datasets and observe a
significant performance boost of our approach.
