  Recent methods utilize graph contrastive Learning within graph-structured
user-item interaction data for collaborative filtering and have demonstrated
their efficacy in recommendation tasks. However, they ignore that the
difference relation density of nodes between the user- and item-side causes the
adaptability of graphs on bilateral nodes to be different after multi-hop graph
interaction calculation, which limits existing models to achieve ideal results.
To solve this issue, we propose a novel framework for recommendation tasks
called Bilateral Unsymmetrical Graph Contrastive Learning (BusGCL) that
consider the bilateral unsymmetry on user-item node relation density for sliced
user and item graph reasoning better with bilateral slicing contrastive
training. Especially, taking into account the aggregation ability of
hypergraph-based graph convolutional network (GCN) in digging implicit
similarities is more suitable for user nodes, embeddings generated from three
different modules: hypergraph-based GCN, GCN and perturbed GCN, are sliced into
two subviews by the user- and item-side respectively, and selectively combined
into subview pairs bilaterally based on the characteristics of inter-node
relation structure. Furthermore, to align the distribution of user and item
embeddings after aggregation, a dispersing loss is leveraged to adjust the
mutual distance between all embeddings for maintaining learning ability.
Comprehensive experiments on two public datasets have proved the superiority of
BusGCL in comparison to various recommendation methods. Other models can simply
utilize our bilateral slicing contrastive learning to enhance recommending
performance without incurring extra expenses.
