GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Exploring folksonomy for personalized search
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
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Nereau: a social approach to query expansion
Proceedings of the 10th ACM workshop on Web information and data management
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Socially filtered web search: an approach using social bookmarking tags to personalize web search
Proceedings of the 2009 ACM symposium on Applied Computing
Exploring social tagging graph for web object classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A content-based method to enhance tag recommendation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Predicting semantic annotations on the real-time web
Proceedings of the 23rd ACM conference on Hypertext and social media
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Social tagging systems have become increasingly a popular way to organize online heterogeneous resources. Tag recommendation is a key feature of social tagging systems. Many works has been done to solve this hard tag recommendation problem and has got same good results these years. Taking into account the complexity of the tagging actions, there still exist many limitations. In this paper, we propose a probabilistic model to solve this tag recommendation problem. The model is based on Bayesian principle, and it's very robust and efficient. For evaluating our proposed method, we have conducted experiments on a real dataset extracted from BibSonomy, an online social bookmark and publication sharing system. Our performance study shows that our method achieves good performance when compared with classical approaches.