  Graph collaborative filtering (GCF) is a popular technique for capturing
high-order collaborative signals in recommendation systems. However, GCF's
bipartite adjacency matrix, which defines the neighbors being aggregated based
on user-item interactions, can be noisy for users/items with abundant
interactions and insufficient for users/items with scarce interactions.
Additionally, the adjacency matrix ignores user-user and item-item
correlations, which can limit the scope of beneficial neighbors being
aggregated.
  In this work, we propose a new graph adjacency matrix that incorporates
user-user and item-item correlations, as well as a properly designed user-item
interaction matrix that balances the number of interactions across all users.
To achieve this, we pre-train a graph-based recommendation method to obtain
users/items embeddings, and then enhance the user-item interaction matrix via
top-K sampling. We also augment the symmetric user-user and item-item
correlation components to the adjacency matrix. Our experiments demonstrate
that the enhanced user-item interaction matrix with improved neighbors and
lower density leads to significant benefits in graph-based recommendation.
Moreover, we show that the inclusion of user-user and item-item correlations
can improve recommendations for users with both abundant and insufficient
interactions. The code is in \url{https://github.com/zfan20/GraphDA}.
