Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations

TL;DR

This paper introduces a novel contrastive learning approach for tripartite graph-based recommender systems, addressing cold-start issues by leveraging high-order similarities through consistency and discrepancy metrics.

Contribution

It proposes a new graph contrastive learning method using meta-path-based metrics to better capture implicit associations in tripartite recommendation graphs.

Findings

Improved recommendation accuracy in cold-start scenarios

Effective modeling of high-order similarities between objects

Enhanced recommendation diversity and relevance

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.