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