Modern Information Retrieval
TRIAS--An Algorithm for Mining Iceberg Tri-Lattices
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Discovering shared conceptualizations in folksonomies
Web Semantics: Science, Services and Agents on the World Wide Web
Logsonomy - social information retrieval with logdata
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the 18th international conference on World wide web
Web Query Recommendation via Sequential Query Prediction
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Reducing Ambiguity in Tagging Systems with Folksonomy Search Expansion
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
A comparison of social bookmarking with traditional search
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Hi-index | 0.00 |
Recently, social bookmarking systems have received surging an increasing attention in both academic and industrial communities. The main thrust of these Web 2.0 systems is their easy use that relies on simple intuitive process, allowing their users to label diverse resources with freely chosen keywords aka tags. The obtained collection are known under the nickname Folksonomy. As these systems grow larger, however, the users address the need of enhanced search facilities. Today, full-text search is supported, but the results are usually simply listed decreasingly by their upload date. Challenging research issue is therefore the development of suitable prediction framework to support users in effectively retrieving the resources matching their real search intents. The primary focus of this paper is to propose a new users' search intent prediction approach for query tag suggestion. Specifically, we adopted Hidden Markov Models and triadic concept analysis to predict users' search intents in a folksonomy. Carried out experiments emphasize the relevance of our proposal and open many issues.