图卷积宽度跨域推荐系统
Graph Convolutional Broad Cross-Domain Recommender System
黄玲 1黄镇伟 1黄梓源 1关灿荣 1高月芳 1王昌栋2
作者信息
- 1. 华南农业大学数学与信息学院 广州 510642
- 2. 中山大学计算机学院 广州 510006
- 折叠
摘要
跨域推荐(cross-domain recommendation,CDR)通过利用其他域的额外知识,有效缓解了传统推荐系统遭遇的数据稀疏性问题.但是当前的CDR方法忽略了用户-项交互图所蕴含的高阶信息.为此,提出了一个新的框架,称为图卷积宽度跨域推荐系统(graph convolutional broad cross-domain recommender system,GBCD).具体地,将传统的用户-项交互的 2-部图扩展到一个(D+1)-部图,以建模每个域中用户和项之间的关系,然后使用公共用户作为源域和目标域之间的桥梁来传递信息.通过图卷积网络(graph convolutional network,GCN)学习用户与项之间的高阶关系,以聚合领域信息.然而,由于GCN在大量节点下收敛速度非常慢,并倾向于吸收不可靠的交互噪声,导致鲁棒性较差.为此,将域聚合特征输入到宽度学习系统(broad learning system,BLS),并利用BLS的随机映射特征增强了GCN的鲁棒性,进而获得了较好的推荐性能.在2个真实数据集上进行的实验结果表明,GBCD优于各种先进的跨域推荐方法.
Abstract
Cross-domain recommendation(CDR)can effectively alleviate the data sparsity problem suffered by the traditional recommendation systems via leveraging additional knowledge from other domains.How to model the interaction information of users and items from the source to target domains is a key issue in CDR.In the current CDR methods,the higher-order information implied by the user-item interaction graph is ignored.To this end,we propose a new framework called graph convolutional broad cross-domain recommender system(GBCD).Specifically,we extend the traditional bipartite graph of user-item interactions to a(D+1)-partite graph to model the relationship between users and items in each domain,and then use common users as a bridge between the source domain and target domain to transfer information.The higher-order relationships between users and items are learned by graph convolutional network(GCN)to aggregate neighbor information.However,GCN converges very slowly with a large number of nodes and tends to absorb unreliable interaction noise,resulting in poor robustness.Therefore,we feed the domain-aggregated features to broad learning system(BLS),which enhances the robustness of GCN by exploiting the stochastic mapping features of BLS,achieving superior recommendation performance.Experiments conducted on two real datasets show that GBCD outperforms the existing state-of-the-art cross-domain recommendation methods.
关键词
跨域推荐/多部图构造/宽度学习系统/图卷积网络/推荐系统Key words
cross-domain recommendation/multipartite graph construction/broad learning system/graph convolutional network/recommender system引用本文复制引用
出版年
2024
NETL
NSTL
万方数据