Convolutional Memory Graph Collaborative Filtering

被引：0
|
作者：
Liu G.-Z. [1 ]
Chen H.-L. [1 ]
机构：
[1] College of Control Science and Engineering, China University of Petroleum(East China), Qingdao
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2021年 / 44卷 / 03期
关键词：
Convolutional neural networks; Gated recurrent unit; Graph neural networks; Rating prediction; Recommender systems;
D O I：
10.13190/j.jbupt.2020-226
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
引用
收藏
页码：21 / 26
页数：5
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