一种基于信任依赖的社会化推荐方法  

SOCIAL RECOMMENDATION METHOD BASED ON TRUST DEPENDENCE

作　　者：徐红主 周从华[1,2] Xu Hongzhu;Zhou Conghua(School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China;New Generation Information Technology Industry Research Institute of Jingkou,Jiangsu University,Zhenjiang 212013,Jiangsu,China)

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]江苏大学京口区新一代信息技术产业研究院,江苏镇江212013

出　　处：《计算机应用与软件》2025年第3期284-290,340,共8页Computer Applications and Software

基　　金：江苏省重点研发计划(社会发展)项目(BE2016630,BE2017628)。

摘　　要：现有模型忽略了社交关系中用户对好友的信任依赖会随兴趣组发生改变的问题。针对这种情况,提出一种社会化推荐方法Social-TD。该算法引入切片层从单一的用户特征中提取不同兴趣组下的用户特征,并使用图神经网络学习不同兴趣组下用户对好友的信任依赖。此外,切片层的引入使得在物品建模时只需考虑当前兴趣组下用户特征,有效降低了传播过程中的噪声信息。两个公开数据集的实验表明,Social-TD算法比其他推荐算法的预测准确性表现更优异,验证了通过兴趣组学习用户信任依赖的有效性。Existing models ignore the problem that the user's trust dependence on friends in social relationships will change with interest groups.To this end,a social recommendation method,Social-TD,is proposed.The algorithm extracted user features in different interest groups from a single user feature by a slice layer,and used a graph neural network to learn the trust dependence of users in different interest groups on friends.In addition,the slice layer allowed only the user feature in the interest group of the current item to be considered when the item was modeled,which effectively reduced the noise information in the propagation process.Experiments on two public datasets show that the Social-TD algorithm performs better than others in predictive accuracy,which verifies the effectiveness of learning user trust dependence through interest groups.

关 键 词：社会化推荐 社交网络 推荐系统 信任依赖 图神经网络 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]