Dual Variational Graph Reconstruction Learning for Social Recommendation

被引:3
作者
Zhang, Yi [1 ]
Zhang, Yiwen [1 ]
Zhao, Yuchuan [1 ]
Deng, Shuiguang [2 ]
Yang, Yun [3 ]
机构
[1] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Anhui, Peoples R China
[2] Zhejiang Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China
[3] Swinburne Univ Technol, Melbourne, Vic 3122, Australia
基金
美国国家科学基金会;
关键词
Social networking (online); Task analysis; Recommender systems; Training; Graph neural networks; Electronic mail; Data models; graph reconstruction; social recommendation; variational inference;
D O I
10.1109/TKDE.2024.3386895
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As a new recommendation pattern combining collaborative filtering and social network, social recommender system strives to introduce auxiliary user relations to alleviate data sparsity problems. Considering the graph structure characteristics of user historical interactions and social network, there have been emerged several innovative works that utilize Graph Neural Network (GNN) for social recommendation to show impressive performance. However, existing works seem to be restricted to exploiting social network as auxiliary information for main recommendation tasks, with little attention on the social network itself at the fine-grained level. From empirical perspective, the effectiveness of directly applying social network to social recommendation via GNNs may be limited since the social information that can be used for training is actually sparser than user interactions, and most of observable social information is not valid. To resolve this problem, we propose a Dual Variational Graph Reconstruction Learning (DVGRL) framework for social recommendation. It treats user interaction graph and social network as equivalent and aims to learn both variational distributions of user preferences from historical interactions and social connections, which are trained simultaneously and used to guide the reconstruction of historical interaction graph and social network. To effectively exploit the social information gleaned from reconstruction learning for enhancing recommendation, we design two inter-domain fusion mechanisms to achieve knowledge transfer from the perspectives of attention features and prior distributions, respectively. Extensive experiments on four real-world datasets validate the effectiveness of DVGRL for social recommendation tasks.
引用
收藏
页码:6002 / 6015
页数:14
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