吉林大学学报（信息科学版）2023，Vol.41Issue(6)：1128-1134,7.
基于深度残差循环神经网络的序列推荐模型
Residual Connected Deep GRU for Sequential Recommendation
王浩宇 1李蕴华1
作者信息
- 1. 吉林大学大数据和网络管理中心,长春 130012
- 折叠
摘要
Abstract
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 sequential recommenders(averagely improved by 8.68％)over all compared metrics.The ablation study verifies the effectiveness of the introduced residual connection,layer normalization and feedforward layer.It is empirically demonstrated that DeepGRU effectively alleviates the unstable training issue when dealing with long sequences.关键词
序列推荐/循环神经网络/门控循环单元/残差网络Key words
sequential recommendation/recurrent neural network/gated recurrent unit/residual network分类
信息技术与安全科学引用本文复制引用
王浩宇,李蕴华..基于深度残差循环神经网络的序列推荐模型[J].吉林大学学报（信息科学版）,2023,41(6):1128-1134,7.
