Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
This work addresses the challenge of modeling hierarchical item transitions for session-based recommendation in online applications like e-commerce, representing an incremental improvement over existing methods.
The paper tackles the problem of capturing complex transition dynamics in session-based recommendation by proposing a multi-task learning framework with multi-level transition dynamics (MTD), which jointly learns intra- and inter-session item transitions hierarchically, achieving superior performance over state-of-the-art baselines on three real-world datasets.
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.