Usage patterns of collaborative tagging systems
Journal of Information Science
Dogear: Social bookmarking in the enterprise
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using annotations in enterprise search
Proceedings of the 15th international conference on World Wide Web
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Can social bookmarking enhance search in the web?
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Exploring social annotations for information retrieval
Proceedings of the 17th international conference on World Wide Web
Efficient top-k querying over social-tagging networks
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Social search and discovery using a unified approach
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Survey on social tagging techniques
ACM SIGKDD Explorations Newsletter
Social bookmark weighting for search and recommendation
The VLDB Journal — The International Journal on Very Large Data Bases
Folksonomy-Based Term Extraction for Word Cloud Generation
ACM Transactions on Intelligent Systems and Technology (TIST)
| Hi-index | 0.00 |
In this work we propose a novel framework for bookmark weighting which allows us to estimate the effectiveness of each of the bookmarks individually. We show that by weighting bookmarks according to their estimated quality we can significantly improve search effectiveness. Using empirical evaluation on real data gathered from two large bookmarking systems, we demonstrate the effectiveness of the new framework for search enhancement.