The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 11th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
Topical link analysis for web search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
Topic-Level Random Walk through Probabilistic Model
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-grain hierarchical topic extraction algorithm for text mining
Expert Systems with Applications: An International Journal
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
Stop thinking, start tagging: tag semantics emerge from collaborative verbosity
Proceedings of the 19th international conference on World wide web
Trend detection in folksonomies
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
A collaborative filtering recommendation system combining semantics and Bayesian reasoning
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. Most social tagging systems order tags just according to the input sequence with little information about the importance and relevance. This limits the applications of tags such as information search, tag recommendation, and so on. In this paper, we pay attention to finding the authority score of tags in the whole tag space conditional on topics and put forward a topic-sensitive tag ranking (TSTR) approach to rank tags automatically according to their topic relevance. We first extract topics from folksonomy using a probabilistic model, and then construct a transition probability graph. Finally, we perform random walk over the topic level on the graph to get topic rank scores of tags. Experimental results show that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into tag recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.