结合跳跃连接的多层图注意力网络会话推荐

基于会话的推荐旨在根据匿名用户的短期交互数据来预测用户下一次交互项目. 现有图神经网络会话推荐模型大多在信息传播过程中平等对待所有邻居节点, 而没有区分他们对于中心节点的重要性, 从而给模型训练引入噪声. 此外, 随着图神经网络层数的增加, 过度平滑问题会随之产生. 针对上述问题, 本文提出结合跳跃连接的多层图注意力网络会话推荐模型(MGATSC). 首先利用图注意力网络学习邻居节点对于中心节点的重要性, 并堆叠多层网络以获取高阶邻居信息; 然后为了缓解过度平滑问题, 采用基于残差注意力机制的跳跃连接更新每层网络的节点嵌入, 并通过平均池化得到最终节点嵌入. 最后将反向位置嵌入融合到节点嵌入中, 经过预测层生成推荐. 在Tmall、Diginetica以及Retailrocket这3个公开数据集上的实验结果表明所提模型优于所有基线模型, 验证了模型的有效性与合理性.

Multi-layer Graph Attention Network with Skip Connection for Session-based Recommendation

Session-based recommendation aims to predict the next interaction item for anonymous users based on short-term interaction data. Most of the existing graph neural network session recommendation models treat all neighboring nodes equally during information propagation without distinguishing their importance to the central node, which introduces noise into the model training. In addition, the problem of over-smoothing arises as the number of layers of graph neural networks increases. To address these issues, a model named multi-layer graph attention network with skip connection for session-based recommendation (MGATSC) is proposed. Firstly, the graph attention network is used to learn the importance of neighboring nodes to the central node, and multiple networks are stacked to obtain high-order neighbor information. Then, to alleviate the over-smoothing problem, a skip connection based on the residual attention mechanism is used to update the node embeddings of each network layer, and the final node embedding is obtained through average pooling. Finally, the reverse positional embedding is fused into the node embedding, and recommendations are generated through the prediction layer. Experimental results on three public datasets, Tmall, Diginetica, and Retailrocket, demonstrate that the proposed model outperforms all baseline models, which validates the effectiveness and rationality of the model.