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Session-based recommendation plays a central role in a wide spectrum ofonline 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 notwell-designed for capturing the complex transition dynamics exhibited withtemporally-ordered and multi-level inter-dependent relation structures. Thesemethods largely overlook the relation hierarchy of item transitional patterns.In this paper, we propose a multi-task learning framework with Multi-levelTransition Dynamics (MTD), which enables the jointly learning of intra- andinter-session item transition dynamics in automatic and hierarchical manner.Towards this end, we first develop a position-aware attention mechanism tolearn item transitional regularities within individual session. Then, agraph-structured hierarchical relation encoder is proposed to explicitlycapture the cross-session item transitions in the form of high-orderconnectivities by performing embedding propagation with the global graphcontext. The learning process of intra- and inter-session transition dynamicsare integrated, to preserve the underlying low- and high-level itemrelationships in a common latent space. Extensive experiments on threereal-world datasets demonstrate the superiority of MTD as compared tostate-of-the-art baselines.

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation