%0 Journal Article
%A CHEN Hong-long
%A LIU Guo-zhen
%T Convolutional Memory Graph Collaborative Filtering
%D 2021
%R 10.13190/j.jbupt.2020-226
%J Journal of Beijing University of Posts and Telecommunications
%P 21-26
%V 44
%N 3
%X An end-to-end graph neural networks with memory unit is proposed for user vector representations and items in recommender systems. Gated recurrent unit is introduced to reduce the information loss between high-order connected nodes. This enables users and items nodes to obtain more complete feature information from high-order neighbor nodes. The convolutional neural networks are used to fuse feature vectors between different output layers to obtain users' preferences at different stages. Experiments on 4 datasets show that compared with the optimal comparison algorithms, the performance of proposed algorithm achieves gain of 1.98%, 4.17%, 9.27% and 2.7%, respectively.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2020-226