轻量且高效：一种带有位置提示的外部注意力图卷积网络用于序列推荐
Lightweight yet Efficient: An External Attentive Graph Convolutional Network with Positional Prompts for Sequential Recommendation
AI总结：
针对图序列推荐计算复杂度高且难以捕捉复杂位置依赖的问题，提出轻量级外部注意力图卷积网络EA-GPS，通过外部记忆单元与位置提示解码器提升推荐效率与性能。
摘要：
基于图的序列推荐系统（GSRs）因能同时处理用户-物品交互和物品间的序列关系而备受研究关注。当前的GSRs通常利用复合或深度的结构进行图编码（例如Graph Transformer）。然而，它们具有较高的计算复杂度，阻碍了在资源受限的边缘设备上的部署。此外，Graph Transformer中的相对位置编码难以考虑序列内复杂的位置依赖关系。为此，我们提出了一种带有位置提示的外部注意力图卷积网络用于序列推荐，即EA-GPS。具体而言，我们首先引入了一个外部注意力图卷积网络，通过两个外部记忆单元线性度量节点间的全局关联。然后，我们提出了一个基于位置提示的解码器，将绝对物品位置显式地作为外部提示。通过引入长度自适应的序列掩码和软注意力网络，该解码器有助于模型捕获序列内的长期位置依赖和上下文关系。在五个真实世界数据集上的大量实验结果表明，所提出的EA-GCS优于最先进的方法。值得注意的是，它在保持更小参数规模和更低训练开销的同时，实现了卓越的性能。该工作的实现已在 https://github.com/ZZY-GraphMiningLab/EA-GPS 上公开。
英文摘要：
Graph-based Sequential Recommender systems (GSRs) have gained significant research attention due to their ability to simultaneously handle user-item interactions and sequential relationships between items. Current GSRs often utilize composite or in-depth structures for graph encoding (e.g., the Graph Transformer). Nevertheless, they have high computational complexity, hindering the deployment on resource-constrained edge devices. Moreover, the relative position encoding in Graph Transformer has difficulty in considering the complicated positional dependencies within sequence. To this end, we propose an External Attentive Graph convolutional network with Positional prompts for Sequential recommendation, namely EA-GPS. Specifically, we first introduce an external attentive graph convolutional network that linearly measures the global associations among nodes via two external memory units. Then, we present a positional prompt-based decoder that explicitly treats the absolute item positions as external prompts. By introducing length-adaptive sequential masking and a soft attention network, such a decoder facilitates the model to capture the long-term positional dependencies and contextual relationships within sequences. Extensive experimental results on five real-world datasets demonstrate that the proposed EA-GPS outperforms the state-of-the-art methods. Remarkably, it achieves the superior performance while maintaining a smaller parameter size and lower training overhead. The implementation of this work is publicly available at https://github.com/ZZY-GraphMiningLab/EA-GPS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
