  Recently, the graph neural network (GNN) has shown great power in matrix
completion by formulating a rating matrix as a bipartite graph and then
predicting the link between the corresponding user and item nodes. The majority
of GNN-based matrix completion methods are based on Graph Autoencoder (GAE),
which considers the one-hot index as input, maps a user (or item) index to a
learnable embedding, applies a GNN to learn the node-specific representations
based on these learnable embeddings and finally aggregates the representations
of the target users and its corresponding item nodes to predict missing links.
However, without node content (i.e., side information) for training, the user
(or item) specific representation can not be learned in the inductive setting,
that is, a model trained on one group of users (or items) cannot adapt to new
users (or items). To this end, we propose an inductive matrix completion method
using GAE (IMC-GAE), which utilizes the GAE to learn both the user-specific (or
item-specific) representation for personalized recommendation and local graph
patterns for inductive matrix completion. Specifically, we design two
informative node features and employ a layer-wise node dropout scheme in GAE to
learn local graph patterns which can be generalized to unseen data. The main
contribution of our paper is the capability to efficiently learn local graph
patterns in GAE, with good scalability and superior expressiveness compared to
previous GNN-based matrix completion methods. Furthermore, extensive
experiments demonstrate that our model achieves state-of-the-art performance on
several matrix completion benchmarks. Our official code is publicly available.
