GSRec: A Graph-Sequence Recommendation System Based on Reverse-Order Graph and User Embedding

Xulin Ma, Jiajia Tan, Linan Zhu, Xiaoran Yan and Xiangjie Kong ()
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Xulin Ma: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Jiajia Tan: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Linan Zhu: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Xiaoran Yan: Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou 310023, China
Xiangjie Kong: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Mathematics, 2024, vol. 12, issue 1, 1-21

Abstract: At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the limitation of the model, the model cannot take advantage of the complete behavioral sequence of the user and cannot know the user’s holistic interests. The accuracy of the model then goes down. Meanwhile, sequence-based models only pay attention to the sequential signals of the data but do not pay attention to the spatial signals of the data, which will also affect the model’s accuracy. This paper proposes a graph sequence-based model called GSRec that combines Graph Convolutional Network (GCN) and Transformer to solve these problems. In the GCN part we designed a reverse-order graph, and in the Transformer part we introduced the user embedding. The reverse-order graph and the user embedding can make the combination of GCN and Transformer more efficient. Experiments on six datasets show that GSRec outperforms the current state-of-the-art (SOTA) models.

Keywords: graph neural network; sequential recommendation; representation learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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