  User-item interactions in recommendations can be naturally de-noted as a
user-item bipartite graph. Given the success of graph neural networks (GNNs) in
graph representation learning, GNN-based C methods have been proposed to
advance recommender systems. These methods often make recommendations based on
the learned user and item embeddings. However, we found that they do not
perform well wit sparse user-item graphs which are quite common in real-world
recommendations. Therefore, in this work, we introduce a novel perspective to
build GNN-based CF methods for recommendations which leads to the proposed
framework Localized Graph Collaborative Filtering (LGCF). One key advantage of
LGCF is that it does not need to learn embeddings for each user and item, which
is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful
CF information into a localized graph and making recommendations based on such
graph. Extensive experiments on various datasets validate the effectiveness of
LGCF especially in sparse scenarios. Furthermore, empirical results demonstrate
that LGCF provides complementary information to the embedding-based CF model
which can be utilized to boost recommendation performance.
