IMGC-GNN: A multi-granularity coupled graph neural network recommendation method based on implicit relationships

被引:0
作者
Qingbo Hao
Chundong Wang
Yingyuan Xiao
Hao Lin
机构
[1] Tianjin University of Technology,School of Computer Science and Engineering
[2] Ministry of Education,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology
[3] Ministry of Education,Engineering Research Center of Learning
来源
Applied Intelligence | 2023年 / 53卷
关键词
Application recommendation; Graph neural network; Context information; Attribute representation; Interaction representation;
D O I
暂无
中图分类号
学科分类号
摘要
In the application recommendation field, collaborative filtering (CF) method is often considered to be one of the most effective methods. As the basis of CF-based recommendation methods, representation learning needs to learn two types of factors: attribute factors revealed by independent individuals (e.g., user attributes, application types) and interaction factors contained in collaborative signals (e.g., interactions influenced by others). However, existing CF-based methods fail to learn these two factors separately; therefore, it is difficult to understand the deeper motivation behind user behaviors, resulting in suboptimal performance. From this point of view, we propose a multi-granularity coupled graph neural network recommendation method based on implicit relationships (IMGC-GNN). Specifically, we introduce contextual information (time and space) into user-application interactions and construct a three-layer coupled graph. Then, the graph neural network approach is used to learn the attribute and interaction factors separately. For attribute representation learning, we decompose the coupled graph into three homogeneous graphs with users, applications, and contexts as nodes. Next, we use multilayer aggregation operations to learn features between users, between contexts, and between applications. For interaction representation learning, we construct a homogeneous graph with user-context-application interactions as nodes. Next, we use node similarity and structural similarity to learn the deep interaction features. Finally, according to the learned representations, IMGC-GNN makes accurate application recommendations to users in different contexts. To verify the validity of the proposed method, we conduct experiments on real-world interaction data from three cities and compare our model with seven baseline methods. The experimental results show that our method has the best performance in the top-k recommendation.
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页码:14668 / 14689
页数:21
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