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Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling

Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling

Edwin Simpson, Mark H. Butler
Copyright: © 2009 |Pages: 22
ISBN13: 9781605663067|ISBN10: 1605663069|ISBN13 Softcover: 9781616924829|EISBN13: 9781605663074
DOI: 10.4018/978-1-60566-306-7.ch003
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MLA

Simpson, Edwin, and Mark H. Butler. "Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling." Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, et al., IGI Global, 2009, pp. 43-64. https://doi.org/10.4018/978-1-60566-306-7.ch003

APA

Simpson, E. & Butler, M. H. (2009). Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling. In M. Chevalier, C. Julien, & C. Soule-Dupuy (Eds.), Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling (pp. 43-64). IGI Global. https://doi.org/10.4018/978-1-60566-306-7.ch003

Chicago

Simpson, Edwin, and Mark H. Butler. "Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling." In Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, Christine Julien, and Chantal Soule-Dupuy, 43-64. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-306-7.ch003

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Abstract

The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of information resources. This chapter investigates methods for enabling improved navigation, user modeling, and personalization using collaboratively generated tags. The authors discuss the advantages and limitations of tags, and describe how relationships between tags can be used to discover latent structures that can automatically organize a collection of tags owned by a community. They give a hierarchical clustering algorithm for extracting latent structure and explain methods for determining tag specificity, then use visualization to examine latent structures. Finally the authors discuss future trends including using latent tag structures to create user models.

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