Graph Isomorphism and Hybrid-Order Residual Gated Graph Neural Network for Session-Based Recommendation
Session-based recommendation aims to predict which item will be clicked next for the current session. Aiming at the shortcomings of existing session recommendation models based on graph neural network, this paper proposes a model named graph isomorphism and hybrid-order residual gated graph neural n...
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| Main Author: | |
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| Format: | Article |
| Language: | zho |
| Published: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2025-02-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2401029.pdf |
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| Summary: | Session-based recommendation aims to predict which item will be clicked next for the current session. Aiming at the shortcomings of existing session recommendation models based on graph neural network, this paper proposes a model named graph isomorphism and hybrid-order residual gated graph neural network for session-based recommendation (GIHR-GNN). Firstly, graph isomorphic network is used to aggregate feature vectors of adjacent items, which can effectively fuse global and local information, solving the problem that graph neural network is good at capturing local connections between nodes and ignoring global information. And the users long-term and short-term interests are aggregated by gated fusion to capture dynamic changes of user interests. Secondly, hybrid-order gated graph neural network is used to process the position embedding to capture the user intention reflected by the users re-interaction after a long time, and the residual module is added to solve the degradation problem of deep network. Finally, contrastive learning is applied to comparing the users long-term interest representations without denoising and after denoising, alleviating the problems of data sparsity and noise interference. Experiments are performed on Tmall and RetailRocket datasets. Compared with baseline model, GIHR-GNN increases at least 3.26% and 10.33% in P@20 and MRR@20 on Tmall, 0.55% and 2.57% in P@20 and MRR@20 on RetailRocket, which proves the effectiveness of GIHR-GNN. |
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| ISSN: | 1673-9418 |