Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation

Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation

Yanan Xu, Yanmin Zhu, Yanyan Shen, Jiadi Yu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 4620-4626. https://doi.org/10.24963/ijcai.2019/642

Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.
Keywords:
Machine Learning Applications: Applications of Supervised Learning
Multidisciplinary Topics and Applications: Information Retrieval
Multidisciplinary Topics and Applications: Recommender Systems