Automatic text processing
KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Usage patterns of collaborative tagging systems
Journal of Information Science
Improved annotation of the blogosphere via autotagging and hierarchical clustering
Proceedings of the 15th international conference on World Wide Web
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
Harvesting social knowledge from folksonomies
Proceedings of the seventeenth conference on Hypertext and hypermedia
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Network properties of folksonomies
AI Communications - Network Analysis in Natural Sciences and Engineering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Extracting key terms from noisy and multitheme documents
Proceedings of the 18th international conference on World wide web
UNIBA: JIGSAW algorithm for word sense disambiguation
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Wikipedia-based semantic interpretation for natural language processing
Journal of Artificial Intelligence Research
Recommending New Tags Using Domain-Ontologies
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
International Journal of Approximate Reasoning
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
Tag recommendation by machine learning with textual and social features
Journal of Intelligent Information Systems
Collaborative topic regression with social regularization for tag recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The rapid growth of the so-called Web 2.0 has changed the surfers' behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users' mental model; secondly, the absence of a controlled vocabulary reduces the users' learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.