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.
@article{arxiv.2407.05126,
title = {Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations},
author = {Linxin Guo and Yaochen Zhu and Min Gao and Yinghui Tao and Junliang Yu and Chen Chen},
journal= {arXiv preprint arXiv:2407.05126},
year = {2024}
}