Personalized Clustering via Targeted Representation Learning

• URL: http://arxiv.org/abs/2412.13690v2
• Date: Fri, 20 Dec 2024 12:08:30 GMT
• Title: Personalized Clustering via Targeted Representation Learning
• Authors: Xiwen Geng, Suyun Zhao, Yixin Yu, Borui Peng, Pan Du, Hong Chen, Cuiping Li, Mengdie Wang,
• Abstract summary: Clustering traditionally aims to reveal a natural grouping structure within unlabeled data. We propose a personalized clustering method that explicitly performs targeted representation learning.
• Score: 12.685373069492448
• License:
• Abstract: Clustering traditionally aims to reveal a natural grouping structure within unlabeled data. However, this structure may not always align with users' preferences. In this paper, we propose a personalized clustering method that explicitly performs targeted representation learning by interacting with users via modicum task information (e.g., $\textit{must-link}$ or $\textit{cannot-link}$ pairs) to guide the clustering direction. We query users with the most informative pairs, i.e., those pairs most hard to cluster and those most easy to miscluster, to facilitate the representation learning in terms of the clustering preference. Moreover, by exploiting attention mechanism, the targeted representation is learned and augmented. By leveraging the targeted representation and constrained contrastive loss as well, personalized clustering is obtained. Theoretically, we verify that the risk of personalized clustering is tightly bounded, guaranteeing that active queries to users do mitigate the clustering risk. Experimentally, extensive results show that our method performs well across different clustering tasks and datasets, even when only a limited number of queries are available.

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