  Tripartite graph-based recommender systems markedly diverge from traditional
models by recommending unique combinations such as user groups and item
bundles. Despite their effectiveness, these systems exacerbate the longstanding
cold-start problem in traditional recommender systems, because any number of
user groups or item bundles can be formed among users or items. To address this
issue, we introduce a Consistency and Discrepancy-based graph contrastive
learning method for tripartite graph-based Recommendation. This approach
leverages two novel meta-path-based metrics consistency and discrepancy to
capture nuanced, implicit associations between the recommended objects and the
recommendees. These metrics, indicative of high-order similarities, can be
efficiently calculated with infinite graph convolutional networks layers under
a multi-objective optimization framework, using the limit theory of GCN.
