Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Exploring social annotations for web document classification
Proceedings of the 2008 ACM symposium on Applied computing
Introduction to Information Retrieval
Introduction to Information Retrieval
Discovering and Modelling Multiple Interests of Users in Collaborative Tagging Systems
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Exploring social tagging graph for web object classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Getting the most out of social annotations for web page classification
Proceedings of the 9th ACM symposium on Document engineering
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
The demographics of web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Applications of web query mining
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
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In social tagging systems the tagging activities of users leave a huge amount of implicit information about them. The users choose tags for the resources they annotate based on their interests, background knowledge, personal opinion and other criteria. Whilst existing research in mining social tagging data mostly focused on gaining a deeper understanding of the user's interests and the emerging structures in those systems, little work has yet been done to use the rich implicit information in tagging activities to unveil to what degree users' tags convey information about their background. The automatic inference of user background information can be used to complete user profiles which in turn supports various recommendation mechanisms. This work illustrates the application of supervised learning mechanisms to analyze a large online corpus of tagged academic literature for extraction of user characteristics from tagging behavior. As a representative example of background characteristics we mine the user's research discipline. Our results show that tags convey rich information that can help designers of those systems to better understand and support their prolific users - users that tag actively - beyond their interests.