Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
Extending a hybrid tag-based recommender system with personalization
Proceedings of the 2010 ACM Symposium on Applied Computing
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Searching and Browsing Tag Spaces Using the Semantic Tag Clustering Search Framework
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Mining social networks for significant friend groups
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Graph based techniques for tag cloud generation
Proceedings of the 24th ACM Conference on Hypertext and Social Media
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In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptors. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major challenge of most social annotation systems resulting from the severe problems of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful approach to handle these problems in the social annotation systems. In this paper, we propose a novel clustering algorithm named kernel information propagation for tag clustering. This approach makes use of the kernel density estimation of the KNN neighbor directed graph as a start to reveal the prestige rank of tags in tagging data. The random walk with restart algorithm is then employed to determine the center points of tag clusters. The main strength of the proposed approach is the capability of partitioning tags from the perspective of tag prestige rank rather than the intuitive similarity calculation itself. Experimental studies on three real world datasets demonstrate the effectiveness and superiority of the proposed method.