  Since very recently, users on the social bookmarking service Delicious can
stack web pages in addition to tagging them. Stacking enables users to group
web pages around specific themes with the aim of recommending to others.
However, users still stack a small subset of what they tag, and thus many web
pages remain unstacked. This paper presents early research towards
automatically clustering web pages from tags to find stacks and extend
recommendations.
