Personalized retrieval in social bookmarking

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 onesizefitsall
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 useraccess
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.