Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
<|reference_start|>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 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.<|reference_end|>
@article{guo2024consistency,
    title={Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning
  for Recommendations},
    author={Linxin Guo, Yaochen Zhu, Min Gao, Yinghui Tao, Junliang Yu, Chen Chen},
    journal={arXiv preprint arXiv:2407.05126},
    year={2024},
    doi={10.1145/3637528.3672056},
    archivePrefix={arXiv},
    eprint={2407.05126},
    primaryClass={cs.IR cs.SI}
}