一种基于图神经网络的社会化推荐算法  

A Social Recommendation Algorithm Based on Graph Neural Network

作　　者：吕艳霞 郝帅 乔广通 邢烨 LYU Yan-xia;HAO Shuai;QIAO Guang-tong;XING Ye(School of Computer&Communication Engineering,Northeastern University at Qinhuangdao,Qinhuangdao 066004,China)

机构地区：[1]东北大学秦皇岛分校计算机与通信工程学院,河北秦皇岛066004

出　　处：《东北大学学报（自然科学版）》2024年第1期10-17,共8页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(61901099);河北省自然科学基金资助项目(F2021501020).

摘　　要：现有的社会化推荐算法大多着眼于用户购买或点击等单一的交互行为,并未同时考虑收藏、浏览等多种不同的交互行为.而且当前的社会化推荐算法重点只关注推荐的准确性,忽略了推荐结果的可解释性.针对以上问题,提出了一种基于图神经网络的社会化推荐算法SRGN,将用户的社交关系和物品间客观存在的语义联系以特定的方式注入到算法架构中,并且利用消息传递的方式实现交互的多行为联合编码,从而提升推荐的准确性.此外,设计了可解释模块为推荐结果提供推荐的理由.在两个真实数据集上与其他8种算法进行对比实验,结果表明提出的算法在推荐性能和用户友好性上具有明显的优势.Most existing social recommendation algorithms focus on the user’s single interaction such as purchase or click,but do not consider different interactions such as collection and browsing simultaneously.Moreover,current social recommendation algorithms only focus on the accuracy of recommendation,ignoring the interpretability of recommendation results.To solve the above problems,a social recommendation algorithm SRGN is proposed based on graph neural network,which injects the social relationships of users and the objectively existing semantic connections between items into the algorithm architecture in a specific way,and jointly encodes the interactive multi-behavior through message transmission,so as to improve the accuracy of recommendation.In addition,an explainable module is designed to provide reasons for the recommendation results.Compared with other eight algorithms on two real datasets,the results show that the proposed algorithm has obvious advantages in recommendation performance and user friendliness.

关 键 词：推荐系统 社会化推荐 图神经网络 可解释推荐 个性化推荐 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]