基于深度残差循环神经网络的序列推荐模型OACSTPCD
Residual Connected Deep GRU for Sequential Recommendation
为解决基于RNN(Recurrent Neural Network)的序列推荐模型在处理长序列时易出现梯度消失或爆炸从而导致推荐模型训练过程不稳定问题,在传统门控循环单元(GRU:Gated Recurrent Unit)基础上,引入了残差连接、层归一化以及前馈神经网络等模块,提出了基于深度残差循环神经网络的序列推荐模型DeepGRU.并在3个公开数据集上进行了验证,实验结果表明,该DeepGRU相较于目前最先进的序列推荐方法具有明显的优势(推荐…查看全部>>
To avoid the gradient vanishing or exploding issue in the RNN(Recurrent Neural Network)-based sequential recommenders,a gated recurrent unit based sequential recommender DeepGRU is proposed which introduces the residual connection,layer normalization and feed forward neural network.The proposed algorithm is verified on three public datasets,and the experimental results show that DeepGRU has superior recommendation performance over several state-of-the-art se…查看全部>>
王浩宇;李蕴华
吉林大学大数据和网络管理中心,长春 130012吉林大学大数据和网络管理中心,长春 130012
计算机与自动化
序列推荐循环神经网络门控循环单元残差网络
sequential recommendationrecurrent neural networkgated recurrent unitresidual network
《吉林大学学报（信息科学版）》 2023 (6)
1128-1134,7
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