Multiple clustering aims to discover diverse latent structures from different perspectives, yet existing methods generate exhaustive clusterings without discerning user interest, necessitating laborious manual screening. Current multi-modal solutions suffer from static semantic rigidity: predefined candidate words fail to adapt to dataset-specific concepts, and fixed fusion strategies ignore evolving feature interactions. To overcome these limitations, we propose Multi-DProxy, a novel multi-modal dynamic proxy learning framework that leverages cross-modal alignment through learnable textual proxies. Multi-DProxy introduces 1) gated cross-modal fusion that synthesizes discriminative joint representations by adaptively modeling feature interactions. 2) dual-constraint proxy optimization where user interest constraints enforce semantic consistency with domain concepts while concept constraints employ hard example mining to enhance cluster discrimination. 3) dynamic candidate management that refines textual proxies through iterative clustering feedback. Therefore, Multi-DProxy not only effectively captures a user's interest through proxies but also enables the identification of relevant clusterings with greater precision. Extensive experiments demonstrate state-of-the-art performance with significant improvements over existing methods across a broad set of multi-clustering benchmarks.

Contrastive Learning (CL) has recently emerged as a powerful technique in recommendation systems, particularly for its capability to harness self-supervised signals from perturbed views to mitigate the data sparsity problem.The process of constructing perturbed views of the user-item bipartite graph and performing CL between perturbed views in a graph convolutional network (GCN) is called graph contrastive learning (GCL).Although existing GCL-based models are effective, the weight assignment method for perturbed views has not been fully explored.A critical problem in existing GCL-based models is the irrational allocation of feature attention.This problem limits the model's ability to effectively leverage crucial features, resulting in suboptimal performance.To address this, we propose a Weighted Graph Contrastive Learning framework (WeightedGCL).Specifically, WeightedGCL applies a robust perturbation strategy, which perturbs only the view of the final GCN layer.In addition, WeightedGCL incorporates a squeeze and excitation network to dynamically weight the features of the perturbed views.Our WeightedGCL strengthens the model's focus on crucial features and reduces the impact of less relevant information.Extensive experiments on widely used datasets demonstrate that our Weight-edGCL achieves significant accuracy improvements compared to competitive baselines.