Personalized Clustering via Targeted Representation Learning

TL;DR

This paper introduces a personalized clustering approach that actively interacts with users to learn targeted representations, improving clustering accuracy by incorporating user preferences and informative pair queries.

Contribution

It presents a novel method combining active user queries, attention mechanisms, and contrastive loss for personalized clustering, with theoretical risk bounds and extensive experimental validation.

Findings

Effective across various datasets and tasks.

Requires limited user queries for high performance.

Theoretically guarantees clustering risk reduction.

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.