Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs

• URL: http://arxiv.org/abs/2503.22241v2
• Date: Mon, 31 Mar 2025 02:56:24 GMT
• Title: Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs
• Authors: Ziye Chen, Yiqun Duan, Riheng Zhu, Zhenbang Sun, Mingming Gong,
• Abstract summary: We propose an agent-centric personalized clustering framework.<n>Agents traverse a relational graph to search for clusters based on user interests.<n>Results show that the proposed method achieves NMI scores of 0.9667 and 0.9481 on the Card Order and Card Suits benchmarks.
• Score: 40.38930402847949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized multiple clustering aims to generate diverse partitions of a dataset based on different user-specific aspects, rather than a single clustering. It has recently drawn research interest for accommodating varying user preferences. Recent approaches primarily use CLIP embeddings with proxy learning to extract representations biased toward user clustering preferences. However, CLIP primarily focuses on coarse image-text alignment, lacking a deep contextual understanding of user interests. To overcome these limitations, we propose an agent-centric personalized clustering framework that leverages multi-modal large language models (MLLMs) as agents to comprehensively traverse a relational graph to search for clusters based on user interests. Due to the advanced reasoning mechanism of MLLMs, the obtained clusters align more closely with user-defined criteria than those obtained from CLIP-based representations. To reduce computational overhead, we shorten the agents' traversal path by constructing a relational graph using user-interest-biased embeddings extracted by MLLMs. A large number of weakly connected edges can be filtered out based on embedding similarity, facilitating an efficient traversal search for agents. Experimental results show that the proposed method achieves NMI scores of 0.9667 and 0.9481 on the Card Order and Card Suits benchmarks, respectively, largely improving the SOTA model by over 140%.

Related papers

• Personalized Clustering via Targeted Representation Learning [12.685373069492448]
  Clustering traditionally aims to reveal a natural grouping structure within unlabeled data.<n>We propose a personalized clustering method that explicitly performs targeted representation learning.
  arXiv  Detail & Related papers  (2024-12-18T10:28:51Z)
• Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning [8.447067012487866]
  We introduce Multi-Sub, a novel end-to-end multiple clustering approach that incorporates a multi-modal subspace proxy learning framework. Our method consistently outperforms existing baselines across a broad set of datasets in visual multiple clustering tasks.
  arXiv  Detail & Related papers  (2024-11-06T15:14:27Z)
• Improved Diversity-Promoting Collaborative Metric Learning for Recommendation [127.08043409083687]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2024-09-02T07:44:48Z)
• Multi-Modal Proxy Learning Towards Personalized Visual Multiple Clustering [8.447067012487866]
  Multi-MaP is a novel method employing a multi-modal proxy learning process. It captures a user's interest via a keyword but also facilitates identifying relevant clusterings. Our experiments show that Multi-MaP consistently outperforms state-of-the-art methods in all benchmark multi-clustering vision tasks.
  arXiv  Detail & Related papers  (2024-04-24T05:20:42Z)
• Self Supervised Correlation-based Permutations for Multi-View Clustering [7.972599673048582]
  We propose an end-to-end deep learning-based MVC framework for general data. Our approach involves learning meaningful fused data representations with a novel permutation-based canonical correlation objective. We demonstrate the effectiveness of our model using ten MVC benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-26T08:08:30Z)
• Large Language Models Enable Few-Shot Clustering [88.06276828752553]
  We show that large language models can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering. We find incorporating LLMs in the first two stages can routinely provide significant improvements in cluster quality.
  arXiv  Detail & Related papers  (2023-07-02T09:17:11Z)
• The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm [154.47590401735323]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2022-09-30T08:02:18Z)
• A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments [54.172993875654015]
  The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments. One-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. For strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean-squared error rates in terms of the sample size.
  arXiv  Detail & Related papers  (2022-09-22T09:04:10Z)
• Personalized Federated Learning via Convex Clustering [72.15857783681658]
  We propose a family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized.
  arXiv  Detail & Related papers  (2022-02-01T19:25:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.