1 research outputs found
Single-Layer Graph Convolutional Networks For Recommendation
Graph Convolutional Networks (GCNs) and their variants have received
significant attention and achieved start-of-the-art performances on various
recommendation tasks. However, many existing GCN models tend to perform
recursive aggregations among all related nodes, which arises severe
computational burden. Moreover, they favor multi-layer architectures in
conjunction with complicated modeling techniques. Though effective, the
excessive amount of model parameters largely hinder their applications in
real-world recommender systems. To this end, in this paper, we propose the
single-layer GCN model which is able to achieve superior performance along with
remarkably less complexity compared with existing models. Our main contribution
is three-fold. First, we propose a principled similarity metric named
distribution-aware similarity (DA similarity), which can guide the neighbor
sampling process and evaluate the quality of the input graph explicitly. We
also prove that DA similarity has a positive correlation with the final
performance, through both theoretical analysis and empirical simulations.
Second, we propose a simplified GCN architecture which employs a single GCN
layer to aggregate information from the neighbors filtered by DA similarity and
then generates the node representations. Moreover, the aggregation step is a
parameter-free operation, such that it can be done in a pre-processing manner
to further reduce red the training and inference costs. Third, we conduct
extensive experiments on four datasets. The results verify that the proposed
model outperforms existing GCN models considerably and yields up to a few
orders of magnitude speedup in training, in terms of the recommendation
performance