The topic-perspective model for social tagging systems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Post-based collaborative filtering for personalized tag recommendation
Proceedings of the 2011 iConference
Personalized topic-based tag recommendation
Neurocomputing
A simple word trigger method for social tag suggestion
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Data Mining and Knowledge Discovery
Tag-aware recommender systems: a state-of-the-art survey
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Topic-driven reader comments summarization
Proceedings of the 21st ACM international conference on Information and knowledge management
Social Link Prediction in Online Social Tagging Systems
ACM Transactions on Information Systems (TOIS)
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Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining. In this paper, we reduce the data complexity in these systems by finding meaningful topics that serve to group similar users and serve to recommend tags or resources to users. We propose a well-founded probabilistic approach that can model every aspect of a collaborative tagging system. By integrating both user information and tag information into the well-known Latent Dirichlet Allocation framework, the developed models can be used to solve a number of important information extraction and retrieval tasks.