CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

Uniform(A). 2: forpolicy 2 do 3: Estimate eV bycallingValEstimate(H, d,D,).

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.