Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913419424169984 |
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| author | Guo, Linxin Zhu, Yaochen Gao, Min Tao, Yinghui Yu, Junliang Chen, Chen |
| author_facet | Guo, Linxin Zhu, Yaochen Gao, Min Tao, Yinghui Yu, Junliang Chen, Chen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05126 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations Guo, Linxin Zhu, Yaochen Gao, Min Tao, Yinghui Yu, Junliang Chen, Chen Information Retrieval Social and Information Networks 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. |
| title | Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations |
| topic | Information Retrieval Social and Information Networks |
| url | https://arxiv.org/abs/2407.05126 |