  We propose an inductive matrix completion model without using side
information. By factorizing the (rating) matrix into the product of
low-dimensional latent embeddings of rows (users) and columns (items), a
majority of existing matrix completion methods are transductive, since the
learned embeddings cannot generalize to unseen rows/columns or to new matrices.
To make matrix completion inductive, most previous works use content (side
information), such as user's age or movie's genre, to make predictions.
However, high-quality content is not always available, and can be hard to
extract. Under the extreme setting where not any side information is available
other than the matrix to complete, can we still learn an inductive matrix
completion model? In this paper, we propose an Inductive Graph-based Matrix
Completion (IGMC) model to address this problem. IGMC trains a graph neural
network (GNN) based purely on 1-hop subgraphs around (user, item) pairs
generated from the rating matrix and maps these subgraphs to their
corresponding ratings. It achieves highly competitive performance with
state-of-the-art transductive baselines. In addition, IGMC is inductive -- it
can generalize to users/items unseen during the training (given that their
interactions exist), and can even transfer to new tasks. Our transfer learning
experiments show that a model trained out of the MovieLens dataset can be
directly used to predict Douban movie ratings with surprisingly good
performance. Our work demonstrates that: 1) it is possible to train inductive
matrix completion models without using side information while achieving similar
or better performances than state-of-the-art transductive methods; 2) local
graph patterns around a (user, item) pair are effective predictors of the
rating this user gives to the item; and 3) Long-range dependencies might not be
necessary for modeling recommender systems.
