• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Wang Lei, Xiong Yuning, Li Yunpeng, Liu Yuanyuan. A Collaborative Recommendation Model Based on Enhanced Graph Convolutional Neural Network[J]. Journal of Computer Research and Development, 2021, 58(9): 1987-1996. DOI: 10.7544/issn1000-1239.2021.20200617
Citation: Wang Lei, Xiong Yuning, Li Yunpeng, Liu Yuanyuan. A Collaborative Recommendation Model Based on Enhanced Graph Convolutional Neural Network[J]. Journal of Computer Research and Development, 2021, 58(9): 1987-1996. DOI: 10.7544/issn1000-1239.2021.20200617

A Collaborative Recommendation Model Based on Enhanced Graph Convolutional Neural Network

Funds: This work was supported by the Research Planning Fund of Humanities and Social Sciences of the Ministry of Education (16XJAZH002) and the Fundamental Research Funds for the Central Universities (JBK2102049).
More Information
  • Published Date: August 31, 2021
  • Graph convolutional network is a deep learning model for graph structure data, and it has become a very hot approach in the research of recommendation system due to its powerful ability in feature extraction and representation of data. This paper focuses on the rating prediction tasks in recommendation system, and points out two deficiencies of existing graph convolutional network based recommendation models by detailed analysis, including making use of first-order collaborative signals only and ignoring the differences of user opinions. For solving them, an end-to-end enhanced graph convolutional network based collaborative recommendation model is proposed. It adopts an enhanced graph convolutional layer to take full advantage of collaborative signals to learn embeddings of users and items on graph, which aggregates second-order collaborative signals and incorporates the influence of different user opinions. And it also stacks several graph convolutional layers to iteratively refine the embeddings and finally uses a nonlinear multilayer perceptron network to make rating prediction. The experiments on 5 benchmark recommendation datasets show that the proposed model achieves lower prediction errors compared with several state-of-the-art graph convolutional network based recommendation models.
  • Related Articles

    [1]Zhang Zilin, Liu Duo, Tan Yujuan, Wu Yu, Luo Longpan, Wang Weilüe, Qiao Lei. An Erasure-Coded Data Update Method for Distributed Storage Clusters[J]. Journal of Computer Research and Development, 2022, 59(11): 2451-2466. DOI: 10.7544/issn1000-1239.20210211
    [2]Chen Jinyin, Huang Guohan, Zhang Dunjie, Zhang Xuhong, Ji Shouling. GRD-GNN: Graph Reconstruction Defense for Graph Neural Network[J]. Journal of Computer Research and Development, 2021, 58(5): 1075-1091. DOI: 10.7544/issn1000-1239.2021.20200935
    [3]Li Guorui, Meng Jie, Peng Sancheng, Wang Cong. A Distributed Data Reconstruction Algorithm Based on Jacobi ADMM for Compressed Sensing in Sensor Networks[J]. Journal of Computer Research and Development, 2020, 57(6): 1284-1291. DOI: 10.7544/issn1000-1239.2020.20190587
    [4]Tang Yingjie, Wang Fang, Xie Yanwen. An Efficient Failure Reconstruction Based on In-Network Computing for Erasure-Coded Storage Systems[J]. Journal of Computer Research and Development, 2019, 56(4): 767-778. DOI: 10.7544/issn1000-1239.2019.20170834
    [5]Fu Yingxun, Wen Shilin, Ma Li, Shu Jiwu. Survey on Single Disk Failure Recovery Methods for Erasure Coded Storage Systems[J]. Journal of Computer Research and Development, 2018, 55(1): 1-13. DOI: 10.7544/issn1000-1239.2018.20160506
    [6]Liu Hai, Li Xinghua, Ma Jianfeng. Rational Secret Sharing Scheme Based on Reconstruction Order Adjustment Mechanism[J]. Journal of Computer Research and Development, 2015, 52(10): 2332-2340. DOI: 10.7544/issn1000-1239.2015.20150511
    [7]Li Yibin, Jia Zhiping, Xie Shuai, and Liu Fucai. Partial Dynamic Reconfigurable WSN Node with Power and Area Efficiency[J]. Journal of Computer Research and Development, 2014, 51(1): 173-179.
    [8]Fan Liya, Zhang Fa, Wang Gongming, Liu Zhiyong. Algorithm Analysis and Efficient Parallelization of the Single Particle Reconstruction Software Package: EMAN[J]. Journal of Computer Research and Development, 2010, 47(12).
    [9]Zhang Hongcan and Xue Wei. Reliability Analysis of Cluster RAID5 Storage System[J]. Journal of Computer Research and Development, 2010, 47(4): 727-735.
    [10]Ma Yili, Fu Xianglin, Han Xiaoming, and Xu Lu. The Separation between Storage and Computation[J]. Journal of Computer Research and Development, 2005, 42(3).
  • Cited by

    Periodical cited type(5)

    1. 张钦宇,张智凯,安丽荣,杨君一,张瑞. 面向天基数据中心的编码修复数据流调度. 移动通信. 2023(07): 21-26 .
    2. 杨浩,李竣业. 电力用户多渠道自动缴费习惯预判预警系统设计. 信息技术. 2021(03): 155-160 .
    3. 包涵,王意洁,许方亮. 基于生成矩阵变换的跨数据中心纠删码写入方法. 计算机研究与发展. 2020(02): 291-305 . 本站查看
    4. 李慧,李贵洋,胡金平,周悦,江小玉,韩鸿宇. 基于分布式存储的OHitchhiker码. 计算机工程与设计. 2020(07): 1941-1946 .
    5. 严新成,陈越,巴阳,贾洪勇,朱彧. 云环境下支持可更新加密的分布式数据编码存储方案. 计算机研究与发展. 2019(10): 2170-2182 . 本站查看

    Other cited types(11)

Catalog

    Article views (830) PDF downloads (534) Cited by(16)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return