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

页码：21 / 26

页数：5

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