Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations

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 long-standing 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 Discrepancybased graph contrastive learning method for tripartite graph-based Recommendation (CDR). This approach leverages two novel metapath-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 (GCN) layers under a multi-objective optimization framework, using the limit theory of GCN. Additionally, we introduce a novel Contrastive Divergence (CD) loss, which can seamlessly integrate the consistency and discrepancy metrics into the contrastive objective as the positive and contrastive supervision signals to learn node representations, enhancing the pairwise ranking of recommended objects and proving particularly valuable in severe cold-start scenarios. Extensive experiments demonstrate the effectiveness of the proposed CDR. The code is released at https://github.com/foodfaust/CDR . CCS CONCEPTS • Information systems → Recommender systems.