  Matrix completion is a classic problem underlying recommender systems. It is
traditionally tackled with matrix factorization. Recently, deep learning based
methods, especially graph neural networks, have made impressive progress on
this problem. Despite their effectiveness, existing methods focus on modeling
the user-item interaction graph. The inherent drawback of such methods is that
their performance is bound to the density of the interactions, which is however
usually of high sparsity. More importantly, for a cold start user/item that
does not have any interactions, such methods are unable to learn the preference
embedding of the user/item since there is no link to this user/item in the
graph. In this work, we develop a novel framework Attribute Graph Neural
Networks (AGNN) by exploiting the attribute graph rather than the commonly used
interaction graph. This leads to the capability of learning embeddings for cold
start users/items. Our AGNN can produce the preference embedding for a cold
user/item by learning on the distribution of attributes with an extended
variational auto-encoder structure. Moreover, we propose a new graph neural
network variant, i.e., gated-GNN, to effectively aggregate various attributes
of different modalities in a neighborhood. Empirical results on two real-world
datasets demonstrate that our model yields significant improvements for cold
start recommendations and outperforms or matches state-of-the-arts performance
in the warm start scenario.
