  The problem of session-based recommendation aims to predict user next actions
based on session histories. Previous methods models session histories into
sequences and estimate user latent features by RNN and GNN methods to make
recommendations. However under massive-scale and complicated financial
recommendation scenarios with both virtual and real commodities , such methods
are not sufficient to represent accurate user latent features and neglect the
long-term characteristics of users. To take long-term preference and dynamic
interests into account, we propose a novel method, i.e. User-Based Embeddings
Recommendation with Graph Neural Network, UBER-GNN for brevity. UBER-GNN takes
advantage of structured data to generate longterm user preferences, and
transfers session sequences into graphs to generate graph-based dynamic
interests. The final user latent feature is then represented as the composition
of the long-term preferences and the dynamic interests using attention
mechanism. Extensive experiments conducted on real Ping An scenario show that
UBER-GNN outperforms the state-of-the-art session-based recommendation methods.
