Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Journal of Machine Learning Research
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Personalized tag suggestion for flickr
Proceedings of the 17th international conference on World Wide Web
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Improving social bookmark search using personalised latent variable language models
Proceedings of the fourth ACM international conference on Web search and data mining
Post-based collaborative filtering for personalized tag recommendation
Proceedings of the 2011 iConference
Personalized topic-based tag recommendation
Neurocomputing
Recent developments in information retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Tag-aware recommender systems: a state-of-the-art survey
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Building Multi-Modal Relational Graphs for Multimedia Retrieval
International Journal of Multimedia Data Engineering & Management
Relational term-suggestion graphs incorporating multipartite concept and expertise networks
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Social tagging systems provide methods for users to categorise resources using their own choice of keywords (or “tags”) without being bound to a restrictive set of predefined terms. Such systems typically provide simple tag recommendations to increase the number of tags assigned to resources. In this paper we extend the latent Dirichlet allocation topic model to include user data and use the estimated probability distributions in order to provide personalised tag suggestions to users. We describe the resulting tripartite topic model in detail and show how it can be utilised to make personalised tag suggestions. Then, using data from a large-scale, real life tagging system, test our system against several baseline methods. Our experiments show a statistically significant increase in performance of our model over all key metrics, indicating that the model could be successfully used to provide further social tagging tools such as resource suggestion and collaborative filtering.