Tagged Documents Co-Clustering
This work addresses the challenge of improving tag-based information retrieval and recommender systems for users, but it appears incremental as it builds on existing co-clustering methods with specific preprocessing steps.
The paper tackled the problem of clustering tags into conceptual groups by preprocessing data to mitigate power-law effects and proposing a hierarchical agglomerative co-clustering algorithm, evaluating it on synthetic and real-world datasets with an unsupervised stopping criterion.
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.