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arXiv · 2407.05126

Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations

Linxin Guo·Yaochen Zhu·Min Gao·Yinghui Tao·Junliang Yu·Chen Chen
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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.

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Linxin Guo, Yaochen Zhu, Min Gao, Yinghui Tao, Junliang Yu, Chen Chen. 2024-07-06. Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations. https://doi.org/10.1145/3637528.3672056

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