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.

翻译：基于三分图的推荐系统与传统模型存在显著差异，其能够推荐独特的组合，例如用户群组与物品捆绑包。尽管此类系统具有良好效果，但它们加剧了传统推荐系统中长期存在的冷启动问题，因为用户或物品之间可以形成任意数量的用户群组或物品捆绑包。为解决这一问题，我们提出一种基于一致性与差异性的图对比学习方法，用于三分图推荐。该方法利用两种新颖的基于元路径的度量指标——一致性与差异性，以捕捉推荐对象与被推荐对象之间细微的隐式关联。这些表征高阶相似性的指标，可在多目标优化框架下，借助图卷积网络的极限理论，通过无限层图卷积网络实现高效计算。