融合生成对抗图卷积网络的社会化推荐算法  

A SOCIAL RECOMMENDATION ALGORITHM COMBINING GENERATE ADVERSARIAL GRAPH CONVOLUTIONAL NETWORK

作　　者：李梁 卫鼎峰 李刚 赵清华 Li Liang;Wei Dingfeng;Li Gang;Zhao Qinghua(Department of Management,Taiyuan Normal University,Jinzhong 030619,Shanxi,China;Micro-Nano System Research Center,College of Information and Computer&Key Lab of Advanced Transducers and Intelligent Control System,Taiyuan University of Technology,Taiyuan 030024,Shanxi,China)

机构地区：[1]太原师范学院管理系,山西晋中030619 [2]太原理工大学信息与计算机学院新型传感器和智能控制教育部(山西)重点实验室微纳系统研究中心,山西太原030024

出　　处：《计算机应用与软件》2025年第1期224-233,共10页Computer Applications and Software

基　　金：国家自然科学基金项目(61901293);山西省应用基础研究计划项目(201901D111099)。

摘　　要：针对显性社交关系的嘈杂性问题以及大多数社会化推荐算法忽略好友之间动态变化的问题,提出一种融合生成对抗图卷积网络的社会化推荐算法(AGCN)。在评分信息和显性社交关系上构建用户的潜在好友关系;利用精简高效的图卷积神经网络学习信息的结构特征,以获取用户和产品的深层次特征;采用生成对抗网络动态地构建与用户具有相同喜好的可信好友,惩戒虚假好友,实现好友的动态变化。在Filmtrust与Ciao数据集上的结果表明,与BPR、SBPR、CUNE-BPR和LightGCN算法相比,无论是普通用户还是冷启动用户,该算法均实现了更好的推荐性能。Aimed at the noisy problem of explicit social relationships and the problem that most social recommendation algorithms ignore the dynamic changes between friends,a social recommendation algorithm(AGCN)fused to generate adversarial graph convolutional networks is proposed.The algorithm built the user's potential friend relationship based on rating information and explicit social relationships,and used a streamlined and efficient graph convolutional neural network to learn the structural features of the information to obtain the deep-level features of users and products.It used a generative confrontation network to dynamically build trusted friends with the same preferences as the user,punished false friends,and achieved dynamic changes in friends.The results on the Filmtrust and Ciao datasets show that compared with the BPR,SBPR,CUNE-BPR,and LightGCN algorithms,this algorithm achieves the best recommendation performance for both ordinary users and cold-start users.

关 键 词：显性社交关系 图卷积神经网络 生成对抗网络 自动编码器 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]