%0 Journal Article
	%A Cheng-Lung Huang and  Han-Yu Chien and  Michael Conyette
	%D 2011
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 54, 2011
	%T Folksonomy-based Recommender Systems with User-s Recent Preferences
	%U https://publications.waset.org/pdf/4481
	%V 54
	%X Social bookmarking is an environment in which
the user gradually changes interests over time so that the tag
data associated with the current temporal period is usually more
important than tag data temporally far from the current period.
This implies that in the social tagging system, the newly tagged
items by the user are more relevant than older items. This study
proposes a novel recommender system that considers the users-
recent tag preferences. The proposed system includes the
following stages: grouping similar users into clusters using an
E-M clustering algorithm, finding similar resources based on
the user-s bookmarks, and recommending the top-N items to
the target user. The study examines the system-s information
retrieval performance using a dataset from del.icio.us, which is
a famous social bookmarking web site. Experimental results
show that the proposed system is better and more effective than
traditional approaches.
	%P 572 - 576