This is the state-of-the-art graph contrastive learning based recommendation method, which proposes randomly node dropout, edge dropout, and random walk for augmentation onthebipartite graph.

Graph convolution networks (GCNs) for recommendations haveemerged asan important research topic due to their ability to exploit higher-order neighbors. Despite their success, most of them suffer from the popularity bias brought by a small number of active users and popular items.

Whilemuchwork has focused on different weight pruning criteria, the overallsparsifiabilityofthe network, i.e., its capacity to be pruned without quality loss, has often been overlooked.

Weintroduce Unbalanced SobolevDescent (USD), aparticle descent algorithm for transporting a high dimensional source distribution to a target distribution that does not necessarily have the same mass.