How people revisit web pages: empirical findings and implications for the design of history systems
International Journal of Human-Computer Studies - Special issue: World Wide Web usability
What do web users do? An empirical analysis of web use
International Journal of Human-Computer Studies
Personalized web search by mapping user queries to categories
Proceedings of the eleventh international conference on Information and knowledge management
Further Experiments on Collaborative Ranking in Community-Based Web Search
Artificial Intelligence Review
Dogear: Social bookmarking in the enterprise
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ASSIST: adaptive social support for information space traversal
Proceedings of the eighteenth conference on Hypertext and hypermedia
Are people biased in their use of search engines?
Communications of the ACM - Alternate reality gaming
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Social tagging roles: publishers, evangelists, leaders
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding the efficiency of social tagging systems using information theory
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Uses of explicit and implicit tags in social bookmarking
Journal of the American Society for Information Science and Technology
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Users of social bookmarking systems take advantage of pivot browsing, an interaction technique allowing them to easily refine lists of bookmarks through the selection of filter terms. However, social bookmarking systems use one-size-fits-all ranking metrics to order refined lists. These generic rankings ignore past user interactions that may be useful in determining the relevance of bookmarks. In this work we describe a personalized ordering algorithm that leverages the fact that refinding, rather than discovery (finding a bookmark for the first time), makes up the majority of bookmark accesses. The algorithm examines user-access histories and promotes bookmarks that a user has previously visited. We investigate the potential of our algorithm using interaction logs from an enterprise social bookmarking system, the results show that our personalized algorithm would lead to improved bookmark rankings.