Graph convolutional networks (GCNs) have become prevalent in recommender...
With the greater emphasis on privacy and security in our society, the pr...
Recent years have witnessed the great successes of embedding-based metho...
Out-of-distribution (OOD) generalization on graphs is drawing widespread...
Most recommender systems optimize the model on observed interaction data...
Learning objectives of recommender models remain largely unexplored. Mos...
Learning powerful representations is one central theme of graph neural
n...
Representation learning on user-item graph for recommendation has evolve...
The latest advance in recommendation shows that better user and item
rep...