TL;DR
The proposed MR-FMVC optimizes fairness alignment and clustering iteratively to enable coordinated optimization and achieves a superior trade-off be-tween clustering performance and fairness.
Multi-view clustering (MVC) aims to uncover latent cluster structures by exploiting complementary information from multiple views. Despite substantial progress in clustering performance, fairness remains an important concern when MVC is applied to socially sensitive scenarios. Recent fair multi-view clustering methods...
Multi-view clustering aims to discover shared semantic structures from multiple complementary data views to improve clustering performance. However, most existing methods rely on a single clustering representation for each semantic cluster, which limits the ability to model complex cluster structures and diverse patter...
Volunteer matching is not well described by conventional one-sided recommendation because effective allocation depends on reciprocal suitability, fairness, interpretability, and sustained participation. Existing studies usually address these objectives separately, and public benchmarks rarely support their joint evalua...
Achieving fairness in machine learning models while maintaining high accuracy is an important but complex task, especially when handling multiple sensitive attributes. Traditional fairness methods often struggle to eliminate bias within subgroups divided by sensitive attributes. Several key challenges have been identif...
Group recommender systems must reconcile collective relevance with divergent member preferences. This study introduces Group-Aware Fair and Diverse Decisions (GA-FDD), a transparent decision-layer framework integrating member-derived and direct-group-history relevance signals with rank-sensitive member utility, model-p...
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