Journal
APPLIED SOFT COMPUTING
Volume 131, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2022.109744
Keywords
Dual -gated graph neural network; Multi -head attention; Long-term preferences; Current interests; Multiple dimensions
Categories
Funding
- National Natural Science Foundation of China [62271341]
- China Scholarship Council
- Key Research and Development Project of Shanxi Province [201901D211313]
- Natural Science Foundation for Young Scientists of Shanxi Province [HGKY2019080]
- Shanxi Scholarship Council of China [2020-127, 2021Y679]
- Shanxi Province Postgraduate Excellent Innovation Project Plan [[2020]1417]
- [201803D421035]
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In this paper, a multi-head attention graph neural network (MAE-GNN) is proposed for session-based recommendation. The model combines a dual-gated graph neural network and multi-head attention mechanisms to extract users' interests and preferences from multiple dimensions, and experimental evaluation shows significant improvement compared to state-of-the-art methods.
In a recent study, it was shown that, with batch training of a graph neural network, it is possible to recommend suitable items for users. Although the method has obtained item embedding and considered the complex transitions between items, there is no multi-dimensional focus on the users' interests and preferences. In this paper, we propose a multi-head attention graph neural network (MAE-GNN) for session-based recommendation by combining a dual-gated graph neural network and multi-head attention mechanisms. MAE-GNN can select important node information and extract users' interests and preferences from multiple dimensions. Experimental evaluation has been conducted to show that, compared with the state-of-the-art methods, the proposed model has significant improvement in term of P@K and MRR@K for session-based recommendation.(c) 2022 Elsevier B.V. All rights reserved.
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