  We consider the problem of learning latent features (aka embedding) for users
and items in a recommendation setting. Given only a user-item interaction
graph, the goal is to recommend items for each user. Traditional approaches
employ matrix factorization-based collaborative filtering methods. Recent
methods using graph convolutional networks (e.g., LightGCN) achieve
state-of-the-art performance. They learn both user and item embedding. One
major drawback of most existing methods is that they are not inductive; they do
not generalize for users and items unseen during training. Besides, existing
network models are quite complex, difficult to train and scale. Motivated by
LightGCN, we propose a graph convolutional network modeling approach for
collaborative filtering CF-GCN. We solely learn user embedding and derive item
embedding using light variant CF-LGCN-U performing neighborhood aggregation,
making it scalable due to reduced model complexity. CF-LGCN-U models naturally
possess the inductive capability for new items, and we propose a simple
solution to generalize for new users. We show how the proposed models are
related to LightGCN. As a by-product, we suggest a simple solution to make
LightGCN inductive. We perform comprehensive experiments on several benchmark
datasets and demonstrate the capabilities of the proposed approach.
Experimental results show that similar or better generalization performance is
achievable than the state of the art methods in both transductive and inductive
settings.
