Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations

Abstract

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.

References in corpus (5)

• LINE: Large-scale Information Network Embedding
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• Personalized Bundle List Recommendation
• ConsRec: Learning Consensus Behind Interactions for Group Recommendation
• Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems