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

• URL: http://arxiv.org/abs/2110.03996v1
• Date: Fri, 8 Oct 2021 09:34:05 GMT
• Title: Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
• Authors: Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang
• Abstract summary: Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. The majority of existing session-based recommendation techniques are not well-designed for capturing the complex transition dynamics exhibited with temporally-ordered and multi-level inter-dependent relation structures. 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.
• Score: 28.656887701954638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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