  Collaborative filtering (CF) is one of the most successful and fundamental
techniques in recommendation systems. In recent years, Graph Neural Network
(GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] have
achieved tremendous success and significantly advanced the state-of-the-art.
While there is a rich literature of such works using advanced models for
learning user and item representations separately, item recommendation is
essentially a link prediction problem between users and items. Furthermore,
while there have been early works employing link prediction for collaborative
filtering [5, 6], this trend has largely given way to works focused on
aggregating information from user and item nodes, rather than modeling links
directly. In this paper, we propose a new linkage (connectivity) score for
bipartite graphs, generalizing multiple standard link prediction methods. We
combine this new score with an iterative degree update process in the user-item
interaction bipartite graph to exploit local graph structures without any node
modeling. The result is a simple, non-deep learning model with only six
learnable parameters. Despite its simplicity, we demonstrate our approach
significantly outperforms existing state-of-the-art GNN-based CF approaches on
four widely used benchmarks. In particular, on Amazon-Book, we demonstrate an
over 60% improvement for both Recall and NDCG. We hope our work would invite
the community to revisit the link prediction aspect of collaborative filtering,
where significant performance gains could be achieved through aligning link
prediction with item recommendations.
