Abstract
In recent years, sequential recommender systems have been widely applied for alleviating information overload. Some solutions employ graph attention networks (GAT) to aggregate rich neighborhood information for the representation learning of items. However, how to sufficiently exploit graph structure deserves careful examination due to two challenges. Firstly, highly related items may not appear in the same interaction sequence due to the data sparsity issue. Secondly, the connection weights among items are randomly initialized, which brings significant uncertainty for information propagation. To tackle these challenges, we propose a novel Neighborhood-Augmented Graph ATtention network (NA-GAT). For the former challenge, we globally screen a fixed number of potential neighbors for each item node based on the attention mechanism. For the latter challenge, we devise a two-stage learning strategy to make full use of the transition frequency and the attention score, to achieve sufficient utilization of graph structure. Extensive experimental results have demonstrated the necessity of neighborhood augmentation and the effectiveness of the proposed NA-GAT framework.
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Acknowledgements
This research has been partially supported by the National Natural Science Foundation of China (No. 62173199).
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Xu, S., Xiang, Q., Fan, Y., Yan, R., Zhang, J. (2024). A Novel Neighborhood-Augmented Graph Attention Network for Sequential Recommendation. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14474. Springer, Singapore. https://doi.org/10.1007/978-981-99-9119-8_21
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