Proceedings of the first ACM international conference on Digital libraries
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Usage patterns of collaborative tagging systems
Journal of Information Science
Proceedings of the 15th international conference on World Wide Web
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
From social bookmarking to social summarization: an experiment in community-based summary generation
Proceedings of the 12th international conference on Intelligent user interfaces
Combating spam in tagging systems
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Can social bookmarking enhance search in the web?
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Automatic labeling of multinomial topic models
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Social tagging systems, such as Delicious, My Web 2.0, Flickr, YouTube, have been very successful and attracted hundreds of million users. User provided tags of an object/page can be used to help the user re-find the object through search or share the customized object with other people.Instead of waiting for a user to find and input the appropriate words to tag an object, we propose to automatically recommend tags for user to choose from, a process that requires much less cognitive effort than traditional tagging. In particular, we formalize the tag suggestion problem as a ranking problem and propose a new probabilistic language model to rank meaningful tags, including words or phrases, for bookmarks. Besides, we adapt the probabilistic language model to tag users. The user tags can be viewed as recommended queries for the user to search documents. They can also be used as meta data about the users, which could be beneficial for people search or person recommendation. The effectiveness of the proposed techniques are demonstrated on data collected from del.icio.us.