  Personalized recommendation is ubiquitous, playing an important role in many
online services. Substantial research has been dedicated to learning vector
representations of users and items with the goal of predicting a user's
preference for an item based on the similarity of the representations.
Techniques range from classic matrix factorization to more recent deep learning
based methods. However, we argue that existing methods do not make full use of
the information that is available from user-item interaction data and the
similarities between user pairs and item pairs. In this work, we develop a
graph convolution-based recommendation framework, named Multi-Graph Convolution
Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple
graphs in the embedding learning process. Multi-GCCF not only expressively
models the high-order information via a partite user-item interaction graph,
but also integrates the proximal information by building and processing
user-user and item-item graphs. Furthermore, we consider the intrinsic
difference between user nodes and item nodes when performing graph convolution
on the bipartite graph. We conduct extensive experiments on four publicly
accessible benchmarks, showing significant improvements relative to several
state-of-the-art collaborative filtering and graph neural network-based
recommendation models. Further experiments quantitatively verify the
effectiveness of each component of our proposed model and demonstrate that the
learned embeddings capture the important relationship structure.
