Tagged Documents Co-Clustering

TL;DR

This paper introduces a hierarchical co-clustering method for tags associated with various resources, aiming to group related tags into meaningful clusters to improve information retrieval and recommendation systems.

Contribution

It presents a novel hierarchical agglomerative co-clustering algorithm for tags, including preprocessing techniques to handle power-law distributions and an unsupervised stopping criterion.

Findings

Effective on synthetic and real-world datasets

Improves tag grouping for better resource retrieval

Handles power-law tag distributions

Abstract

Tags are short sequences of words allowing to describe textual and non-texual resources such as as music, image or book. Tags could be used by machine information retrieval systems to access quickly a document. These tags can be used to build recommender systems to suggest similar items to a user. However, the number of tags per document is limited, and often distributed according to a Zipf law. In this paper, we propose a methodology to cluster tags into conceptual groups. Data are preprocessed to remove power-law effects and enhance the context of low-frequency words. Then, a hierarchical agglomerative co-clustering algorithm is proposed to group together the most related tags into clusters. The capabilities were evaluated on a sparse synthetic dataset and a real-world tag collection associated with scientific papers. The task being unsupervised, we propose some stopping criterion for selectecting an optimal partitioning.