Call for papers

Representation Learning and Clustering (RLC)

In conjunction with  WSDM 2024  March. 4 - 8 2024, Mérida, Yucatán, Mexico

Technical areas

The RLC workshop intends to promote research at the intersection of representation learning and clustering, and its application to real-life data science challenges. The workshop welcomes both high-quality academic (theoretical or empirical) and practical papers on unsupervised graph representation learning for clustering and related work. For reference, here is a non-exclusive list of topics of interest:

Technical areas

• Deep clustering models

• Unsupervised feature learning

• Graph neural networks

• Manifold learning

• Feature learning

• Representation learning

• Spectral data embedding

• Deep Mixtures models

• Multi-view clustering

• Semi-supervised deep learning

• self-supervised learning

• Attributed networks

Application areas

In addition, to attract researchers from various communities, this workshop will encourage submissions on applications, especially those that motivate the development of deep clustering models or comparisons of classical approaches and deep approaches , such as

• Recommender Systems

• Graph mining

• Computer Vision

• Information Retrieval

• Large Language Models

• Bioinformatics

• Web mining