  Session-based recommendation plays a central role in a wide spectrum of
online applications, ranging from e-commerce to online advertising services.
However, the majority of existing session-based recommendation techniques
(e.g., attention-based recurrent network or graph neural network) are not
well-designed for capturing the complex transition dynamics exhibited with
temporally-ordered and multi-level inter-dependent relation structures. These
methods largely overlook the relation hierarchy of item transitional patterns.
In this paper, we propose a multi-task learning framework with Multi-level
Transition Dynamics (MTD), which enables the jointly learning of intra- and
inter-session item transition dynamics in automatic and hierarchical manner.
Towards this end, we first develop a position-aware attention mechanism to
learn item transitional regularities within individual session. Then, a
graph-structured hierarchical relation encoder is proposed to explicitly
capture the cross-session item transitions in the form of high-order
connectivities by performing embedding propagation with the global graph
context. The learning process of intra- and inter-session transition dynamics
are integrated, to preserve the underlying low- and high-level item
relationships in a common latent space. Extensive experiments on three
real-world datasets demonstrate the superiority of MTD as compared to
state-of-the-art baselines.
