Proceedings

Aligning Contrastive Multiple Clusterings with User Interests

Shan Zhang, Liangrui Ren, Jun Wang, Yanyu Xu, Carlotta Domeniconi, Guoxian Yu

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7011-7019.

https://doi.org/10.24963/ijcai.2025/780

Keywords

  • Machine Learning: ML: Clustering
  • Machine Learning: ML: Multi-view learning
  • Machine Learning: ML: Multi-label learning

Abstract

Multiple clustering approaches aim to partition complex data in different ways. These methods often exhibit a one-to-many relationship in their results, and relying solely on the data context may be insufficient to capture the patterns relevant to the user. User’s expectation is key for the multiple clustering task. Two main challenges exist: identifying the significant features to represent user interests and aligning those interests with the clustering results. To address this issue, we propose Contrastive Multiple Clusterings (CMClusts), which extends contrastive learning to multiple clustering by elevating traditional instance-level contrast to clustering-level contrast. Furthermore, CMClusts integrates user expectations or interests by extracting desired features through tailored data augmentations, enabling the model to effectively capture user-relevant clustering features. Experimental results on benchmark datasets show that CMClusts can generate interpretable and high-quality clusterings, which reflect different user interests.

Shan Zhang, Liangrui Ren, Jun Wang, Yanyu Xu, Carlotta Domeniconi, and Guoxian Yu. 2025. Aligning Contrastive Multiple Clusterings with User Interests. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7011–7019. https://doi.org/10.24963/ijcai.2025/780
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